This paper proposes a novel noise-shaping (NS) successive approximation register (SAR) based light-to-digital converter (LDC) for continuous multi-wavelength (MW) photoplethysmogram (PPG) monitoring. The proposed NS-SAR LDC employs a single-channel current-integration (CI) SAR quantizer with a three-channel passive NS loop filter performs time-multiplexed, low-power NS readout for MWPPG. The cascade integrator feedforward NS architecture features a passive nested feedback path, which contributes one additional NS order. The LDC enables a wide dynamic range (DR) through the high signal-to-noise-and-distortion ratio (SNDR), inherent AC gain control of the proposed CI NS-SAR, and a predictive threshold filter that compensates for large DC and drifting baseline currents induced by motion artifacts and interference light. Fabricated in a 180-nm standard CMOS process, the NS-SAR LDC consumes$26.5~\mu $W while achieving a SNDR of 95 dB and a total DR of 161.5 dB. The work is validated through finger MWPPG measurements using commercial PPG sensors.

Long-term cuffless monitoring of blood pressure (BP) is of immense value in the diagnosis and prevention of cardiovascular diseases (CVD). While there are critical needs for wearable cuffless BP monitoring devices in personalized CVD care and hypertension management, current technologies face challenges in long-term accuracy which limits significantly its clinical applicability. To maintain the accuracy of cuffless blood pressure forecasting, frequent calibration is a vital necessity. However, conventional BP calibration approaches are inherently inconvenient and time-consuming. In this work, we address the challenge by developing a novel auto-calibration system enabling long-term BP estimation. The innovation of this approach combines depth-resolved vascular characteristics extracted from multi-wavelength photoplethysmographic (MWPPG) measurements at fingertip, with an adaptive cross-modal knowledge distillation algorithm for continuous refinement of blood pressure estimation. We have evaluated the system on the CAS-BP database of over a thousand subjects using various calibration strategies. The results have shown that frequent calibration can effectively enhance the accuracy of long-term blood pressure estimation. Compared to traditional manual calibration, the proposed auto-calibration method can perform frequent calibration of the model without requiring the true BP values. Furthermore, under the condition of daily auto-calibration, the system attains an optimal Mean Absolute Error (MAE) of 3.77 mmHg and 2.62 mmHg for systolic BP (SBP) and diastolic BP (DBP) respectively, significantly outperforming the performance achieved through traditional calibration methods. This work provides a novel conceptual framework to guide the calibration of cuffless wearable devices for BP monitoring.

Blood pressure (BP) measurement accuracy is highly sensitive to sensor placement. To address this, we investigated the effect of multi-sensor sites on BP estimation using a 9-channel single-wavelength photoplethysmography (PPG) sensor array placed within a localized wrist area. As a starting point, we analyzed the variability of 9 PPG features across channels, revealing notable site-specific variations, with amplitude-based features showing greater sensitivity. Leveragingthese these findings, we developed 3M-BPNet, which processes PPG signals through varying channel/site counts. The network incorporates signal trimming and Bayesian optimization for channel-weight allocation, followed by a Random Forest Regression (RFR) model with personalized calibration. Tested on 121 subjects, the 3M-BPNet achieved mean absolute errors (MAEs) of 4.84 mmHg for systolic BP (SBP) and 3.28 mmHg for diastolic BP (DBP), outperforming a standard RFR model. Notably, BP estimation accuracy improved as the channel counts increased from 1 to 5, then declined beyond 5. The optimal 5-channel combination (C5-C6-C7-C8-C9), located near the radial artery, yielded MAEs of 1.33 mmHg for SBP and 1.16 mmHg for DBP, corresponding to accuracy gains of 72.6% for SBP and 73.2% for DBP over the single-channel setup (P < 0.001 for both MAE_SBP and MAE_DBP). Compared with the best prior PPG-based BP estimation results, our method reduced SBP MAE by 71.9% and DBP MAE by 51.3%. These findings highlight the critical role of sensor location in PPG-based BP estimation, suggesting that optimized sensor placement can enhance the accuracy and guide the design of wearable cuffless BP devices, thereby advancing hypertension management.

Electrocardiography (ECG) is the gold standard for monitoring heart function and is crucial for preventing the worsening of cardiovascular diseases (CVDs). However, the inconvenience of ECG acquisition poses challenges for long-term continuous monitoring. Consequently, researchers have explored non-invasive and easily accessible photoplethysmography (PPG) as an alternative, converting it into ECG. Previous studies have focused on peaks or simple mapping to generate ECG, ignoring the inherent periodicity of cardiovascular signals. This results in an inability to accurately extract physiological information during the cycle, thus compromising the generated ECG signals' clinical utility. To this end, we introduce a novel PPG-to-ECG translation model called CATransformer, capable of adaptive modeling based on the cardiac cycle. Specifically, CATransformer automatically extracts the cycle using a cycle-aware module and creates multiple semantic views of the cardiac cycle. It leverages a transformer to capture detailed features within each cycle and the dynamics across cycles. Our method outperforms existing approaches, exhibiting the lowest RMSE across five paired PPG-ECG databases. Additionally, extensive experiments are conducted on four cardiovascular-related tasks to assess the clinical utility of the generated ECG, achieving consistent state-of-the-art performance. Experimental results confirm that CATransformer generates highly faithful ECG signals while preserving their physiological characteristics.

Fine-tuning large-scale pre-trained models provides an effective solution to alleviate the label scarcity problem in cardiovascular diseases (CVDs) detection using electrocardiogram (ECG). However, as the pre-trained models scale up, the computational costs for fine-tuning and inference become unaffordable on low-level devices deployed for clinical applications. Additionally, maintaining the model performance under low budgets in computational resources remains a significant challenge. However, a comprehensive study that can address them in a joint framework is still lacking. Here, we propose a holistic method (H-Tuning) for low-cost and efficient fine-tuning of pre-trained models on downstream datasets. Then, the inference costs of the models fine-tuned by H-Tuning are further reduced significantly using a knowledge distillation technique. Experiments on four ECG datasets demonstrate that H-Tuning reduces the GPU memory consumption during fine-tuning by 6.34 times while achieving comparable CVDs detection performance to standard fine-tuning. With the knowledge distillation technique, the model inference latency and the memory consumption are reduced by 4.52 times and 19.83 times. As such, the proposed joint framework allows for the utilization of pre-trained models with high computation efficiency and robust performance, exploring a path toward low-cost and efficient CVDs detection. Code is available at https://github.com/KAZABANA/H-Tuning

This work presents a low-power, large cross-scale dynamic range (DR) successive approximation register (SAR) based light-to-digital converter (LDC) designed for wearable multi-wavelength photoplethysmogram (MWPPG) readout. The proposed SAR-LDC employs a current-integration SAR (CI-SAR) quantizer that directly integrates the photocurrent onto a binary capacitive digital-to-analog converter (DAC) and utilizes low-power SAR logic for direct digitization. This approach releases the various trade-offs present in the recent LDC designs and further maximizes the LDC’s power efficiency. At the circuit level, an AC/DC correlated DR enhancement loop, controlled by a digital reconfigurable unit (DRU), enhances both the DC and AC DR of the proposed system. The proposed architecture is fabricated in standard 180-nm CMOS technology. By utilizing a 10-bit SAR-LDC, it achieves a maximum effective number of bits (ENOB) of 9.93 and 12.56 in the Nyquist bandwidth and PPG bandwidth, respectively. The SAR-LDC reads MWPPG on a single channel through time multiplexing, consuming 9.84 μW only while achieving a cross-scale AC DR of 103.4 dB and a total DR of 148.9 dB without using any hybrid circuit technique. The proposed SAR-LDC is further validated using with a commercial PPG sensor for finger MWPPG measurement.

Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0.242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.

Wearable cuffless blood pressure (BP) technology is emerging as a critical tool for monitoring hypertension, the leading risk factor of most cardiovascular diseases. However, current cuffless BP methods are not accurate enough for clinical use, because they mainly use single or dual modalities/features as inputs for estimation. To address this challenge, we propose multimodal McBP-Net, built with hybrid CNN-LSTM architecture combing two-layer convolution operations with four-layer LSTMs to capture both local signal features and temporal dependencies for continuous dynamic beat-to-beat BP estimation. The McBP-Net includes photoplethysmographic, electrocardiographic, impedanceplethysmographic (IPG), and skin temperature (ST) signals as inputs. Validated on 23 subjects undergoing cold pressor test to induce large BP variability, the McBP-Net achieves the mean absolute errors of 4.19 and 2.98 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively, which fall within the accuracy range required by the Grade A of IEEE standard. The integration of four multimodal signals improves performance by 16.20%, 37.37%, and 49.52% over three-, dual-, and single-modality approaches, respectively, with significant contributions from IPG and ST signals. Notably, ST shows a strong nonlinear relationship with BP with high mutual information of 0.9056 for SBP. Furthermore, McBP-Net achieves a reasonable balance between accuracy and computational efficiency, offering inference speed of 36.7% faster and reducing computational demands by 78% compared to transformer-based models tested. Importantly, it maintains robust performance, with only a 0.21 mmHg degradation in dynamic SBP estimation when trained on rest-stage data. McBP-Net demonstrates promising potential in medical-grade wearable cuffless dynamic BP measurements.

The real-time tracking of human physiopathology states can significantly enhance the quality of personalized healthcare services. Photoplethysmography (PPG) detection is a rapid, portable and non-invasive method for measuring blood flow volume, widely used for monitoring blood pressure (BP) and cardiovascular status. However, continuous BP monitoring technologies based on PPG face numerous challenges in real-world wearable scenarios, such as poor signal quality, complex model computation, and the need for frequent calibration. This work proposed a personalized continuous BP tracking pipeline that performed automatic PPG signal quality grading to reduce the difficulty of model fitting, introduced a lightweight BP model (SCI-GTCN) to alleviate computational complexity, and employed an adaptive calibration strategy to achieve long-term BP monitoring performance under different scenarios. The proposed pipeline was validated using data from 134 subjects in various monitoring scenarios (daytime, nighttime, and abnormal states), assessing the model's performance during rapid BP changes, circadian rhythm fluctuations, and long-term monitoring. The ME±SD was 0.99±7.91/0.36±5.43 mmHg. Overall, the results of our method are within the accuracy requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards, though the subject distribution differs. The method demonstrated good robustness and applicability, making it convenient for deployment on wearable devices and promising in the healthcare field.

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this article, we propose a novel semisupervised transfer learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an EEG-Mixup-based data augmentation method is developed to generate more valid samples for model learning. Second, a semisupervised two-step pairwise learning method is proposed to bridge prototypewise and instancewise pairwise learning, where the prototypewise pairwise learning measures the global relationship between EEG data and the prototypical representation of each emotion class and the instancewise pairwise learning captures the local intrinsic relationship among EEG data. Third, a semisupervised multidomain adaptation is introduced to align the data representation among multiple domains (labeled source domain, unlabeled source domain, and target domain), where the distribution mismatch is alleviated. Extensive experiments are conducted on three benchmark databases (SEED, SEED-IV, and SEED-V) under a cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show the proposed EEGMatch performs better than the state-of-the-art methods under different incomplete label conditions (with 5.89% improvement on SEED, 0.93% improvement on SEED-IV, and 0.28% improvement on SEED-V), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals. The source code is available at https://github.com/KAZABANA/EEGMatch.

Federated learning (FL) has a great potential in large-scale machine learning applications by training a global model over distributed client data. However, FL deployed in real-world applications often incur collaboration bias and unstable convergence with inconsistent local predictions, resulting in poor modelling performance on heterogeneous and long-tailed client data distributions. In this paper, we reconsider heterogeneous FL in a two-stage learning paradigm where representation learning and classifier re-training are separated to incorporate different sampling schemes. This allows us to deal with the dilemma of obtaining more generalizable features and fine tuning a biased classifier building on client model aggregations. Specifically, we propose a novel hybrid knowledge distillation scheme, called FedHyb, to facilitate the two-stage learning. From the view of knowledge transfer, we show that FedHyb enables several desirable properties in the global feature space and optimization with fine-tuning, thus achieving better test accuracy and convergence speed, especially with a higher level of data heterogeneity and an increasing number of distributed clients. FedHyb does not require any information exchange between clients preventing privacy leakage, and is more robust under poisoning attacks comparing with other FL methods designed on heterogeneous data.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Affective brain-computer interface based on electroencephalography (EEG) is an important branch in the field of affective computing. However, the individual differences in EEG emotional data and the noisy labeling problem in the subjective feedback seriously limit the effectiveness and generalizability of existing models. To tackle these two critical issues, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning (PR-PL). The discriminative and generalized EEG features are learned for emotion revealing across individuals and the emotion recognition task is formulated as pairwise learning for improving the model tolerance to the noisy labels. More specifically, a prototypical learning is developed to encode the inherent emotion-related semantic structure of EEG data and align the individuals' EEG features to a shared common feature space under consideration of the feature separability of both source and target domains. Based on the aligned feature representations, pairwise learning with an adaptive pseudo labeling method is introduced to encode the proximity relationships among samples and alleviate the label noises effect on modeling. Extensive results on two benchmark databases (SEED and SEED-IV) under four different cross-validation evaluation protocols validate the model reliability and stability across subjects and sessions. Compared to the literature, the average enhancement of emotion recognition across four different evaluation protocols is 2.04% (SEED) and 2.58% (SEED-IV). The source code is available athttps://github.com/KAZABANA/PR-PL.

Noninvasive blood glucose (BG) measurement could significantly improve the prevention and management of diabetes. In this paper, we present a robust novel paradigm based on analyzing photoplethysmogram (PPG) signals. The method includes signal pre-processing optimization and a multi-view cross-fusion transformer (MvCFT) network for non-invasive BG assessment. Specifically, a multi-size weighted fitting (MSWF) time-domain filtering algorithm is proposed to optimally preserve the most authentic morphological features of the original signals. Meanwhile, the spatial position encoding-based kinetics features are reconstructed and embedded as prior knowledge to discern the implicit physiological patterns. In addition, a cross-view feature fusion (CVFF) module is designed to incorporate pairwise mutual information among different views to adequately capture the potential complementary features in physiological sequences. Finally, the subject- wise 5- fold cross-validation is performed on a clinical dataset of 260 subjects. The root mean square error (RMSE) and mean absolute error (MAE) of BG measurements are 1.129 mmol/L and 0.659 mmol/L, respectively, and the optimal Zone A in the Clark error grid, representing none clinical risk, is 87.89%. The results indicate that the proposed method has great potential for homecare applications.

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement. Conversely, photoplethysmography (PPG) data are typically measured passively by consumer devices, and therefore available for long-period monitoring and suitable in duration for identifying transient cardiac events. However, classifying or predicting cardiac abnormalities from the PPG is very difficult, because it is a peripherally-measured signal. Hence, the use of the PPG for predictive inference is often limited to deriving physiological parameters (heart rate, breathing rate, etc.) or for obvious abnormalities in cardiac timing, such as atrial fibrillation/flutter ("palpitations"). This work aims to combine the best of both worlds: using continuously-monitored, near-ubiquitous PPG to identify periods of sufficient abnormality in the PPG such that prompting the user to take an ECG would be informative of cardiac risk. We propose a dual-convolutional-attention network (DCA-Net) to achieve this ECG-based PPG classification. With DCA-Net, we prove the plausibility of this concept on MIMIC Waveform Database with high performance level (AUROC 0.9 and AUPRC 0.7) and receive satisfactory result when testing the model on an independent dataset (AUROC 0.7 and AUPRC 0.6) which it is not perfectly-matched to the MIMIC dataset.

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.

Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. “Contact tracing” is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COVID-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules—1) case maps; 2) exposure detection; 3) screening; and 4) health indicators that take multiple inputs like users’ self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users’ risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the Web-based information on confirmed COVID-19 cases and screening tools, which potentially increases the adoption rate of the system.

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.

Adding cuffless blood pressure (BP) measurement function to wearable devices is of great value in the fight against hypertension. The widely used arterial pulse transit time (PTT)-based method for BP monitoring relies primarily on vascular status-determined BP models and typically exhibits degraded performance over time and is sensitive to measurement procedures. Developing alternative methods with improved accuracy and adaptability to various application scenarios is highly desired for cuffless BP measurement. In this work, we proposed a pattern-fusion (PF) method that incorporates cardiovascular coupling effects in the vascular model by combining three calculation modules - cardiac parameter extraction module, cardiac parameter-to-BP mapping module, and BP regulation module. Specifically, the first module combines feedforward, feedback, and propagation modes to model different modulation functions of a cardiovascular system and is responsible for extracting BP-related features from electrocardiography (ECG) and photoplethysmography (PPG) signals; the cardiac parameter-to-BP mapping module is used to map cardiac parameters into mean blood pressure (MBP) by fusing different features; finally, the BP regulation module recovers accurate systolic BP (SBP) and diastolic BP (DBP) from given MBP. With the concerted use of these three modules, the pattern fusion method consistently demonstrates excellent BP prediction accuracy in a variety of measurement scenarios and durations, exhibiting SBP/DBP mean absolute error (MAE) of 3.65/4.56 mmHg for the short-term (<10 mins) continuous measurement dataset, SBP/DBP MAE of 6.84/3.81 mmHg for the medium-term (avg. > 20 hours) continuous measurement dataset, and SBP/DBP MAE of 6.24/3.65 mmHg for the long-term (>1 month) intermittent measurement dataset.

With the development of modern cameras, more physiological signals can be obtained from portable devices like smartphone. Some hemodynamically based non-invasive video processing applications have been applied for blood pressure classification and blood glucose prediction objectives for unobtrusive physiological monitoring at home. However, this approach is still under development with very few publications. In this paper, we propose an end-to-end framework, entitled cocktail causal container, to fuse multiple physiological representations and to reconstruct the correlation between frequency and temporal information during multi-task learning. Cocktail causal container processes hematologic reflex information to classify blood pressure and blood glucose. Since the learning of discriminative features from video physiological representations is quite challenging, we propose a token feature fusion block to fuse the multi-view fine-grained representations to a union discrete frequency space. A causal net is used to analyze the fused higher-order information, so that the framework can be enforced to disentangle the latent factors into the related endogenous association that corresponds to down-stream fusion information to improve the semantic interpretation. Moreover, a pair-wise temporal frequency map is developed to provide valuable insights into extraction of salient photoplethysmograph (PPG) information from fingertip videos obtained by a standard smartphone camera. Extensive comparisons have been implemented for the validation of cocktail causal container using a Clinical dataset and PPG-BP benchmark. The root mean square error of 1.329±0.167 for blood glucose prediction and precision of 0.89±0.03 for blood pressure classification are achieved in Clinical dataset.

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.

Deep learning (DL) models have demonstrated great potential in cuffless blood pressure (BP) estimation under static conditions, while the performance under dynamic conditions was still not fully validated. This study developed a DL model using population data for training and followed by individualized fine-tuning to directly learn features from multisensory signals including electrocardiogram (ECG), photoplethysmogram (PPG) and PPG derivatives for beat-to-beat BP estimation under water drinking. 25 healthy subjects were recruited, and the leave-one-subject-out approach was used to evaluate the model performance. The results showed that individualized fine-tuning using a small amount of individual baseline data did not change the tracking capability of the model, while can largely reduce the individual bias in dynamic BP estimation, with the mean absolute errors decreased from 13.43 to 9.49 mmHg and 8.48 to 5.54 mmHg for systolic BP and diastolic BP, respectively. It was also found that the model presented better results around the baseline BP levels than that at larger deviations from the baseline, indicating that future work should incorporate individual dynamic data in the fine-tuning to improve dynamic BP estimation further.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which is measured with electrocardiography and photoplethysmography (PPG). To achieve cuffless BP monitoring with more compact wearable electronics, we have previously conceived a multi-wavelength PPG (MWPPG) strategy to perform BP estimation from arteriolar PTT, requiring only a single sensing node. However, challenges remain in decoding the compounded MWPPG signals consisting of both heterogeneous physiological information and motion artifact (MA). In this work, we proposed an improved MWPPG algorithm based on principal component analysis (PCA) which matches the statistical decomposition results with the arterial pulse and capillary pulse. The arteriolar PTT is calculated accordingly as the phase shift based on the entire waveforms, instead of local peak lag time, to enhance the feature robustness. Meanwhile, the PCA-derived MA component is employed to identify and exclude the MA-contaminated segments. To evaluate the new algorithm, we performed a comparative experiment (N = 22) with a cuffless MWPPG measurement device and used double-tube auscultatory BP measurement as a reference. The results demonstrate the accuracy improvement enabled by the PCA-based operations on MWPPG signals, yielding errors of 1.44 ± 6.89 mmHg for systolic blood pressure and -1.00 ± 6.71 mm Hg for diastolic blood pressure. In conclusion, the proposed PCA-based method can improve the performance of MWPPG in wearable medical devices for cuffless BP measurement.

Capillary blood pressure (CBP) is the primary driving force for fluid exchange across microvessels. Subclinical systemic venous congestion prior to overt peripheral edema can directly result in elevated peripheral CBP. Therefore, CBP measurements can enable timely edema control in a variety of clinical cases including venous insufficiency, heart failure and so on. However, currently CBP measurements can be only done invasively and with a complicated experimental setup. In this work, we proposed an opto-mechanical system to achieve non-invasive and automatic CBP measurements through modifying the widely implemented oscillometric technique in home-use arterial blood pressure monitors. The proposed CBP system is featured with a blue light photoplethysmography sensor embedded in finger/toe cuffs to probe skin capillary pulsations. The experimental results demonstrated the proposed CBP system can track local CBP changes induced by different levels of venous congestion. Leveraging the decision tree technique, we demonstrate the use of a multi-site CBP measurement at fingertips and toes to classify four categories of subjects (total N = 40) including patients with peripheral arterial disease, varicose veins and heart failure. Our work demonstrates the promising non-invasive CBP measurement as well as its great potential in realizing point-of-care systems for the management of cardiovascular diseases.

Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 ± 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10 s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultra-short-term ECG recordings suitable for wearable and mobile health applications in living-free environments.

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.

The articles in this special section focus on flexible sensing and medical imaging for cerebro-cardiovascular health care services. Healthcare and disease management are receiving increasing attention. Cerebro-cardiovascular diseases (CCVDs) are the leading cause of death globally. Cerebrocardiovascular diseases include a variety of medical conditions that affect the blood vessels of the brain, the cerebral circulation, and the heart. The common presentations of CCVDs include an ischemic stroke or mini-stroke and sometimes a hemorrhagic stroke, heart failure, hypertensive heart disease, etc. The important contributing risk factors include high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, and excessive alcohol consumption, among others. A rapidly growing field, biomedical and health engineering research for CCVDs is unique in that it involves a variety of specialties such as neurology, surgery, cardiology, psychology and rehabilitation, and must meet the growing need for sophisticated, up-to-date biomedical and health informatics on clinical data, diagnostic testing, and therapeutic issues.

The four papers in this special section focus on the Internet of medical things for healthcare. The current market needs to provide low-cost and convenient biomedical diagnosis as well as proactive health management and smart healthcare service to the elderly people and patientswith chronic illnesses are growing tremendously. Therefore, extensive researches have been dedicated to the development of novel Internet of Medical Things (IoMT) technologies and applications for healthcare engineering, which are leading to a new and promising healthcare strategy transform and a paradigm shift from hospital-centered to patient-centered, and from disease-focus to health-focus. The papers in this section are dedicated to the state-of-the-art IoMT in health engineering related topics, and emphasizes the bioelectronics, biophysics, biochemistry, and microsystems related topics for healthcare engineering.

The most commonly used oscillometric upper-arm (UA) blood pressure (BP) monitors are not convenient enough for ambulatory BP monitoring, given the large size of the arm cuff and the compression of UA during the measurement. Finger-worn oscillometric BP devices featuring miniaturized finger cuff have been developed and researched as an alternative solution to the UA-based measurement, yet the reliability of the finger-based measurement is still questioned. To investigate the feasibility of oscillometric BP measurements at the finger position, we performed model-based analysis and experimental validation to explore the underlying issues associated with extending the cuff-based oscillometric approach from UA to other alternative sites. The simulation results revealed that a larger bone-to-tissue volume ratio produced a lower pressure transmission efficiency, which can account for the inter-site measurement discrepancies of mean blood pressure (MBP). We also experimentally compared the oscillometric MBP measurements at UA, middle forearm, wrist, finger proximal phalanx, and finger distal phalanx (FD) of 20 young adults, and each position was matched with a cuff of appropriate size and kept at the same height with the heart. The experimental results demonstrated that FD could be a superior alternative position for oscillometric BP measurement, as it requires the smallest cuff size while providing the most consistent MBP with the UA. Our analysis also suggested that further study is demanded to identify the appropriate oscillometric algorithm for reliable systolic blood pressure and diastolic blood pressure measurements at FD.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

The seven papers in this special section examine the latest advances in the field of integrated precision medicine technologies. In the majority of medical conditions common therapeutic approaches are usually effective in only a small percentage of the patient population. Recent scientific discoveries implicate as possible causes of this lack of effect the multifactorial nature of most diseases and the patient variability in disease expression, genetic disposition, and environmental exposures. It has become apparent that in order to improve the response to therapy and long term prognosis, treatment must be specifically tailored to the disease and the patient. Precision medicine is an attempt to maximize effectiveness by taking into account individual variability in clinical presentation, medical history, genes, environment, and lifestyle. It is a leap beyond the promise of “personalization” empowered by recent technological advances. However, progress in precision medicine has been slow due to the lack of “precision” in the traditional research, translation, and clinical practice. Current approaches are largely empirical, fragmented, lack integration, and rely on population statistics, with inadequate feedback between disciplines. In addition, most of the information required for personalization is either missing or unutilized. New technological developments can help overcome these hurdles of imprecision to achieve the full promise of precision medicine.

For many cerebrovascular diseases both blood pressure (BP) and hemodynamic changes are important clinical variables. In this paper, we describe the development of a novel approach to noninvasively and simultaneously monitor cerebral hemodynamics, BP, and other important parameters at high temporal resolution (250 Hz sampling rate). In this approach, cerebral hemodynamics are acquired using near infrared spectroscopy based sensors and algorithms, whereas continuous BP is acquired by superficial temporal artery tonometry with pulse transit time based drift correction. The sensors, monitoring system, and data analysis algorithms used in the prototype for this approach are reported in detail in this paper. Preliminary performance tests demonstrated that we were able to simultaneously and noninvasively record and reveal cerebral hemodynamics and BP during people's daily activity. As examples, we report dynamic cerebral hemodynamic and BP fluctuations during postural changes and micturition. These preliminary results demonstrate the feasibility of our approach, and its unique power in catching hemodynamics and BP fluctuations during transient symptoms (such as syncope) and revealing the dynamic features of related events.

The aging of population has been recognized as one of the top grand global challenges. Age-related diseases such as neurodegenerative diseases, cardiovascular and cerebrovascular diseases, and psychological diseases have become the primary killers of human and consume the major portion of healthcare resources due to the long course of disease and large patient base. Powered by the technologies originated from manufacturing industries driven by the fourth revolution of industry (Industry 4.0), the fourth revolution in healthcare technologies (Healthcare 4.0) is also happening as envisioned by Pang et al. [item 1) in the Appendix]. In the Healthcare 4.0, vast amount of cyber and physical systems (CPS) are closely combined through the Internet of Things (IoT), intelligent sensing, big data analytics, artificial intelligence, cloud computing, automatic control, and autonomous execution and robotics to create not only digitalized healthcare products and technologies but also digitalized healthcare services and enterprises. Driven by these mega trends, the Health Engineering (i.e., the applications of engineering principles and convenience approaches to solve problems in health) is emerging as a new interdisciplinary field of research and development. This convergence research model provides a blueprint for addressing society’s most pressing health challenges and leads to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases, so that preemptive treatment can be delivered to realize personalized, precision, pervasive, and patient-centralized healthcare,

Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustness of the proposed approach were validated for static, dynamic, and follow-up performance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation, with a correlation coefficient and mean error of 0.852 and -0.001 ± 3.102 mmHg for systolic BP, and 0.790 and -0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree one day after model construction but was relatively stable from one day to six months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.

The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intraarterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intraarterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.

Recent advances in unobtrusive sensing technology, especially those in flexible, stretchable, and printable sensing, have given rise to various novel signal acquisition modalities, such as stretchable epidermal electrocardiography (ECG), organic photoplethysmography (PPG), and flexible tonoarteriography (TAG) which is the cuffless and continuous recording of arterial blood pressure (BP). With the fast development of wearable computing and wireless communication technologies, all these modalities can be integrated into a body sensor network (BSN) for remote physiological multi-parameter monitoring. In this paper, we propose a TAG-based BSN for unobtrusive BP measurement with possible automatic cuffless BP calibration, and our efforts focus on the effect of posture change on the various pulse transit time (PTT) calculated from different BSN nodes consisting of TAG, ECG, and PPG sensors. Specifically, correlations of different PTTs with reference continuous BP at different postures are examined. The results of this study demonstrate that the PTT from ECG and TAG sensors has higher correlation with the reference BP as compared to that from ECG and PPG sensors, which suggests that flexible TAG sensor may potentially be utilized not only for cuffless calibration, but also as an alternative node in the BSN for continuous, cuffless BP measurement with better accuracy.

Summary form only given. Health informatics deals with the acquisition, transmission, processing, storage, and retrieval of information to enhance the quality and efficiency of healthcare such that it becomes personalized, preventive, predictive, pre-emptive, participatory, pervasive, and precise. A major direction in health informatics is to research and develop technologies that enable the identification of health issues at an early stage. This often requires novel and innovative approaches that can be launched for mass surveillance of a large population. Sensing and analysis of physiological signals remains to be one of the core techniques in clinical screening. Many physiological parameters, such as blood pressure, have been proven to be key risk factors of diseases. With recent advancements in sensors and communication networks, measurement of these parameters does not have to be limited to the short periods an individual spent during ad-hoc infrequent clinic visits. Rather, low-cost sensors can be worn by individuals or integrated into the living environment such that health can be managed unobtrusively for extensively long periods. Substantial technical challenges remain to be solved before these methods can be adopted into routine clinical practice, e.g., to invent new sensing principles enabling the unobtrusive monitoring of physiological and health conditions, to ensure the security and privacy of health information from the sensor to system levels to seamlessly connect the sensors of a user to a body sensor network, as well as to design a framework architecture for the overall e-Health services.

Presents an introduction to this new publication and explains is scope, focus, and planned contents.

These tutorials/keynote speeches discuss the following: the effects of nicotine exposure on the complexity and the genetic patterns of dopamine neurons in VTA; from 6-Ps medicine to cardiovascular health informatics; computer-aided interpretation of vascular images towards valid diagnosis and risk stratification of atherosclerosis; turning data into predictions of gene and protein function; from reading to writing (and rewriting) the code of life: the future of biology - scientific, ethical, legal, civil and social issues.

Medical devices and medical electronics are areas that had little to offer 100 years ago. However, there were three important existing technologies that led to many further developments over the following 100 years. These are the stethoscope, electrocardiography, and X-ray medical imaging. Although these technologies had been described and were available to some extent when the Proceedings of the IEEE pages first appeared, they had yet to achieve the widespread use that they have today. The stethoscope is the oldest of these, and it helped physicians to hear sounds of the body and relate them to functioning and malfunctioning organs. The early use of the stethoscope by physicians was more of an art than a science, but as the Proceedings matured, so did this technology. Engineers were able to make this a more quantitative process by graphically displaying the sounds and ultimately using techniques such as voiceprint analysis to assist the physician in diagnosis and monitoring of treatment. The electrocardiograph had been invented a few years prior to the appearance of the Proceedings, but the apparatus was awkward to use, especially for sick people, and was considered more of an oddity than a viable medical technology 100 years ago. Today, it and devices derived from it such as cardiac patient monitors are important parts of our healthcare system. Similarly, X-rays represented a new technology 100 years ago, but unlike electrocardiography physicians immediately saw the value of this technology and quickly adopted it. Many improvements have been made to the basic technology over the last 100 years culminating in computer tomography and complex image processing. Other devices to create high-quality and 3-D medical images have also been developed in recent years to make medical imaging a very important aspect of clinical care today. Looking to the future is always a difficult task, but it is clear that the electronic health record will play an important role in consolidating the information from various medical devices as well as providing readily available data on patients wherever it might be needed. Future medical devices will need to not only address the problems of diagnostic and therapeutic medicine but also be capable of addressing important societal problems such as worldwide disparities in the availability of medical care, continually rising healthcare costs, and healthcare for travel beyond Earth. The next 100 years promises to be even more exciting than the last from the perspective of medical devices and medical electronics.

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.

In the last decade, the seminal term and concept of "m-health" were first defined and introduced in this transactions as "mobile computing, medical sensor, and communications technologies for healthcare." Since that special section, the m-health concept has become one of the key technological domains that reflected the key advances in remote healthcare and e-health systems. The m-health is currently bringing together major academic research and industry disciplines worldwide to achieve innovative solutions in the areas of healthcare delivery and technology sectors. From the wireless communications perspective, the current decade is expected to bring the introduction of new wireless standards and network systems with true mobile broadband and fast internet access healthcare services. These will be developed around what is currently called the fourth-generation (4G) mobile communication systems. In this editorial paper, we will introduce the new and novel concept of 4G health that represents the long-term evolution of m-health since the introduction of the concept in 2004. The special section also presents a snapshot of the recent advances in these areas and addresses some of the challenges and future implementation issues from the evolved m-health perspective. It will also present some of the concepts that can go beyond the traditional "m-health ecosystem" of the existing systems. The contributions presented in this special section represent some of these developments and illustrate the multidisciplinary nature of this important and emerging healthcare delivery concept.

Cardiovascular disease (CVD) has become the primary killer worldwide and is expected to cause more deaths in the future. Prediction and prevention of CVD have therefore become important social problems. Many groups have developed prediction models for asymptomatic CVD by classifying its risk based on established risk factors (e.g., age, sex, etc.). More recently, studies have uncovered that many genetic variants are associated with CVD outcomes/traits. If treated as single or multiple risk factors, the genetic information could improve the performance of prediction models as well as promote the development of individually tailored risk models. In this paper, eligible genome-wide association studies for CVD outcomes/traits will be overviewed. Clinical trials on CVD prediction using genetic information will be summarized from overall aspects. As yet, most of the single or multiple genetic markers, which have been evaluated in the follow-up clinical studies, did not significantly improve discrimination of CVD. However, the potential clinical utility of genetic information has been uncovered initially and is expected for further development.

The IEEE Transactions on Information Technology in Biomedicine (T-ITB) will be retitled as the IEEE Journal of Biomedical and Health Informatics (J-BHI) starting in January, 2013. The IEEE Transactions on Information Technology in Biomedicine was launched with four issues per year in 1997. After more than a decade of steady growth, the journal ventured into new challenges: open access of publication and rapid expansion in all health informatics-related fields. In this concluding issue of T-ITB the authors would like to take this opportunity to report briefly some historical retrospectives of journal with known statistics of it and to extend a special appreciation from the current editorial office to readers, authors, reviewers, associate editors, Engineering in Medicine and Biology Society (EMBS) relevant committees, IEEE publication staff, and especially two previous Editors-in-Chief for their great contributions to make the Transactions successful. The launching of the J-BHI as the new title of T-ITB is an outcome of the vision of the EMBS's 2011 Publications Committee. The title change and scope revision were accomplished through more than two years of team efforts by T-ITB Editorial Board, EMBS Executive Office, Publication Committee, and AdCom. In the next editorial to be published on January issue of J-BHI, we will discuss the rationale behind the title change and scope revision as well as the grand challenges in health informatics with future perspectives.

Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a biometric BS on a WBSN node is on average 23 ms and memory occupation is 204 bytes for any given IPI sequence. The results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such that key distribution over the WBSN can be avoided.

The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultralow-power processing/communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This editorial paper presents a snapshot of the current research and emerging applications and addresses some of the challenges and implementation issues.

Using the development of the Cardiovascular Health Informatics and Multimodal E-record (CHIME) as an example, the authors have discussed in this editorial note four goals, each with a corresponding technical challenge in health informatics that needs to be overcome in order to better prevent cardiovascular disease (CVD). With collaborative efforts, it is hoped that these barriers and all those unmentioned can be removed such that the problem of CVD can be relieved. Since CVD and common chronic diseases share a number of risk factors, the experience in the development of CHIME will definitely be valuable in formulating solutions to the crisis created by the ageing population and the prevalence of chronic disease on our health care system. Thus we are looking forward to receiving original contributions to T-ITB in the area of health informatics to expediate progress in this area.

Security of the emerging body sensor network (BSN) in telemedicine applications is a crucial problem because personal medical information must be protected against flaws and misdeeds. The solution is, however, nontrivial because lightweight mechanisms have to be deployed to meet the stringent resource constraints of these networks. It has been suggested that the inherent ability of human body to transfer information is a unique and resource-saving method to secure wireless communications within a BSN. For example, physiological characteristics can be captured by individual sensors of a BSN to generate entity identifiers (EIs) for identifying nodes and even securing keying materials, i.e., by a biometric approach. This study demonstrates the performance analysis of such a biometric trait, i.e., the interpulse intervals (IPIs) of heartbeats that were calculated from electrocardiogram and photoplethysmogram of 99 subjects. Based on the characteristics of IPIs, a lightweight generation scheme of EIs is proposed. Individual randomness and group similarity of the generated EIs are then evaluated. False acceptance rate and false rejection rate are also calculated to measure the effectiveness of the proposed identification system. The results suggest that the readily available IPI information can be a good source for generating EIs among BSN nodes.

Reduction of motion artifact and power saving are crucial in designing a wearable pulse oximeter for long-term telemedicine application. In this paper, a novel algorithm, minimum correlation discrete saturation transform (MCDST) has been developed for the estimation of arterial oxygen saturation (SaO2), based on an optical model derived from photon diffusion analysis. The simulation shows that the new algorithm MCDST is more robust under low SNRs than the clinically verified motion-resistant algorithm discrete saturation transform (DST). Further, the experiment with different severity of motions demonstrates that MCDST has a slightly better performance than DST algorithm. Moreover, MCDST is more computationally efficient than DST because the former uses linear algebra instead of the time-consuming adaptive filter used by latter, which indicates that MCDST can reduce the required power consumption and circuit complexity of the implementation. This is vital for wearable devices, where the physical size and long battery life are crucial.

In this paper, an electrocardiographic (ECG) signal processing IC, which is used for portable biomedical application, was designed using continuous-time technique. The circuit consists of an instrumentation amplifier (INA) with driven-right-leg circuit (DRL), a 5th order G/sub m/-C low pass filter (G/sub m/-C LPF) operating in sub-threshold mode, and amplifiers. DRL circuit is used to detect small amplitude signal in the presence of large common-mode voltage from the human body. The CMRR of the INA is 78 dB and the G/sub m/-C LPF has a cutoff frequency of 18 Hz. As a result of using the DRL, a small signal can be detected in the presence of large common-mode differential. The circuit consumes 1.23 mW when operating from with a supply voltage of /spl plusmn/1.5-V and occupies a core area of 0.94 mm/sup 2/. The circuit was designed in a 0.35/spl mu/m CMOS process and simulation results have successfully demonstrated the functionalities.

Many parties have already demonstrated telemedicine applications that use cellular phones and the Internet. A current trend in telecommunication is the convergence of wireless communication and computer network technologies, and the emergence of wireless application protocol (WAP) devices is an example. Since WAP will also be a common feature found in future mobile communication devices, it is worthwhile to investigate its use in telemedicine. This paper describes the implementation and experiences with a WAP-based telemedicine system for patient-monitoring that has been developed in our laboratory. It utilizes WAP devices as mobile access terminals for general inquiry and patient-monitoring services. Authorized users can browse the patients' general data, monitored blood pressure (BP), and electrocardiogram (ECG) on WAP devices in store-and-forward mode. The applications, written in wireless markup language (WML), WMLScript, and Perl, resided in a content server. A MySQL relational database system was set up to store the BP readings, ECG data, patient records, clinic and hospital information, and doctors' appointments with patients. A wireless ECG subsystem was built for recording ambulatory ECG in an indoor environment and for storing ECG data into the database. For testing, a WAP phone compliant with WAP 1.1 was used at GSM 1800 MHz by circuit-switched data (CSD) to connect to the content server through a WAP gateway, which was provided by a mobile phone service provider in Hong Kong. Data were successfully retrieved from the database and displayed on the WAP phone. The system shows how WAP can be feasible in remote patient-monitoring and patient data retrieval.

Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.

The effects of interfering signals in the knee and the need for an adaptive filtering scheme for knee-sound estimation are briefly discussed. A least-mean-squares algorithm has been used for adaptive cancellation of interfering signals in knee sounds. In the implementation, a two-stage adaptive scheme is used to cancel both muscle contraction and tremor artifacts. Experiments show that with the proposed filtering scheme the interfering signals are significantly reduced, while retaining the desired features of the knee-sound signals. This will subsequently allow accurate quantitative analysis and objective diagnosis of cartilage pathology.>