Multimodal emotion recognition has emerged as a promising direction for capturing the complexity of human affective states by integrating physiological and behavioral signals. However, challenges remain in addressing feature redundancy, modality heterogeneity, and insufficient inter-modal supervision. In this paper, we propose a novel Multimodal Disentangled Knowledge Distillation framework that explicitly disentangles modality-shared and modality-specific features and enhances cross-modal knowledge transfer via a graph-based distillation module. Specifically, we introduce a dual-stream representation learning architecture that separates common and unique subspaces across modalities. To facilitate effective information interaction, we design a directed and learnable modality graph, where each edge represents the semantic transfer strength from one modality to another. We validate our method on two benchmark datasets-MAHNOB-HCI and DEAP-for both regression and classification tasks, under subject-dependent and subject-independent protocols. Experimental results demonstrate that our method achieves state-of-the-art performance, with statistical significance confirmed by paired two-tailed $t$-tests. In addition, qualitative analysis of the learned modality graph and t-SNE embeddings further illustrates the effectiveness of our feature disentanglement and dynamic knowledge transfer design. This work offers a unified, interpretable, and robust framework for multimodal emotion understanding and lays the foundation for affective computing in real-world human-machine interaction scenarios.

In recent years, the demand for smart healthcare solutions have heightened the need for accuracy, reliability, and comfort in bedside ECG recording and analysis. This study presents a bedside non-direct contact ECG recording system based on capacitive coupling electrocardiography (cECG) and verifies its performance in accurately capturing Heart Rate Variability (HRV) during the night. Firstly, cECG collects ECG data through clothing, avoiding skin irritation from conventional wet electrodes. Secondly, leveraging the unique characteristics of cECG signals, a deep learning framework assesses the quality of cECG, filtering noise and identifying off-bed information, enhancing HRV analysis precision. Subsequently, the system was employed to recording sleep data from 6 subjects overnight, with our proposed algorithm utilized for signal quality assessment (SQA) and HRV analysis. Finally, HRV features were compared with synchronously collected wet electrode ECG signals, encompassing time domain features, frequency domain features, and nonlinear features, totaling 13 HRV features. Experimental findings demonstrate that for the SQA task, the model achieved a classification accuracy of 94.7%, with a Recall of 0.941, Precision of 0.940, F1 score of 0.941, and Cohen's Kappa of 0.927. The accuracy of on/off-bed monitoring reached 99.79%. Additionally, HRV features showed a strong correlation with the reference ECG. In the time-domain metrics, the largest mean absolute percentage error (MAPE) is for PNN50, with a value of 8.148%. In the frequency-domain features, the largest MAPE is for HF, with a value of 13.253%. For nonlinear features, the largest MAPE is for SD1, with a value of 5.182%. Generally, the system exhibited a reliable solution for cECG recording, on/off-bed status detection, and bedside HRV analysis.

SpeechDepressionDetection (SDD) has garnered attention from researchers due to its low cost and convenience. However, current algorithms lack methods for extracting interpretable acoustic features based on clinical manifestations. In addition, effectively fusing these features to overcome individual heterogeneity remains a challenge. This study proposes a decoupled multi-perspective fusion (DMPF) model. The model extracts five key features of voiceprint, emotion, pause, energy, and tremor based on the multi-perspective clinical manifestations. These features are then decoupled into common and private features, which fused through graph attention network to obtain the comprehensive depression representation. Notably, this study has collected a depression speech dataset, which includes standardized and comprehensive tasks along with diagnostic labels provided by psychologists. Extensive subject-independent experiments were conducted on the DAIC-WOZ, MODMA and MPSC datasets. The voiceprint features can automatically cluster the depressed and non-depressed populations. Furthermore, DMPF can effectively fuse common and private features from different perspectives, achieving AUC of 84.20%, 85.34%, 86.13% on three datasets. The results illustrate the interpretability of multi-perspective features and demonstrate that the combination of speech manifestations can enhance the detection ability, which can provide a multi-perspective observational tool for physicians and clinical practice.

Complexity measurement, essential in diverse fields like finance, biomedicine, climate science, and network traffic, demands real-time computation to mitigate risks and losses. Sample Entropy (SampEn) is an efficacious metric which quantifies the complexity by assessing the similarities among microscale patterns within the time-series data. Unfortunately, the conventional implementation of SampEn is computationally demanding, posing challenges for its application in real-time analysis, particularly for long time series. Field Programmable Gate Arrays (FPGAs) offer a promising solution due to their fast processing and energy efficiency, which can be customized to perform specific signal processing tasks directly in hardware. The presented work focuses on accelerating SampEn analysis on FPGAs for efficient time-series complexity analysis. A refined, fast, Lightweight SampEn architecture (LW SampEn) on FPGA, which is optimized to use sorted sequences to reduce computational complexity, is accelerated for FPGAs. Various sorting algorithms on FPGAs are assessed, and novel dynamic loop strategies and micro-architectures are proposed to tackle SampEn's undetermined search boundaries. Multi-source biomedical signals are used to profile the above design and select a proper architecture, underscoring the importance of customizing FPGA design for specific applications. Our optimized architecture achieves a 7x to 560x speedup over standard baseline architecture, enabling real-time processing of time-sensitive data.

Automated sleep staging is crucial for assessing sleep quality and diagnosing sleep-related diseases. Single-channel EEG has attracted significant attention due to its portability and accessibility. Most existing automated sleep staging methods often emphasize temporal information and neglect spectral information, the relationship between sleep stage contextual features, and transition rules between sleep stages. To overcome these obstacles, this paper proposes an attention-based two stage temporal-spectral fusion model (BiTS-SleepNet). The BiTS-SleepNet stage 1 network consists of a dual-stream temporal-spectral feature extractor branch and a temporal-spectral feature fusion module based on the cross-attention mechanism. These blocks are designed to autonomously extract and integrate the temporal and spectral features of EEG signals, leveraging temporal-spectral fusion information to discriminate between different sleep stages. The BiTS-SleepNet stage 2 network includes a feature context learning module (FCLM) based on Bi-GRU and a transition rules learning module (TRLM) based on the Conditional Random Field (CRF). The FCLM optimizes preliminary sleep stage results from the stage 1 network by learning dependencies between features of multiple adjacent stages. The TRLM additionally employs transition rules to optimize overall outcomes. We evaluated the BiTS-SleepNet on three public datasets: Sleep-EDF-20, Sleep-EDF-78, and SHHS, achieving accuracies of 88.50%, 85.09%, and 87.01%, respectively. The experimental results demonstrate that BiTS-SleepNet achieves competitive performance in comparison to recently published methods. This highlights its promise for practical applications.

As the scenarios for electrocardiogram (ECG) monitoring become increasingly diverse, particularly with the development of wearable ECG, the influence of ambiguous factors in diagnosis has been amplified. Reliable ECG information must be extracted from abundant noises and confusing artifacts. To address this issue, we suggest an uncertainty-inspired model for beat-level diagnosis (UI-Beat). The base architecture of UI-Beat separates heartbeat localization and event diagnosis in two branches to address the problem of heterogeneous data sources. To disentangle the epistemic and aleatoric uncertainty within one stage in a deterministic neural network, we propose a new method derived from uncertainty formulation and realize it by introducing the class-biased transformation. Then the disentangled uncertainty can be utilized to screen out noise and identify ambiguous heartbeat synchronously. The results indicate that UI-Beat can significantly improve the performance of noise detection (from 91.60% to 97.50% for real-world noise detection and from 61.40% to 82.41% for real-world artifact detection). For multi-lead ECG analysis, UI-Beat is approaching the performance upper bound in heartbeat localization (only 15 false positives and 9 false negatives out of the 175,907 heartbeats in the INCART database) and achieving a significant performance improvement in heartbeat classification through uncertainty-based cross-lead fusion compared to single-lead prediction and other state-of-the-art methods (an average improvement of 14.28% for detecting heartbeats of S and 3.37% for detecting heartbeats of V). Considering the characteristic of one-stage ECG analysis within one model, it is suggested that the proposed UI-Beat has the potential to be employed as a general model for arbitrary scenarios of ECG monitoring, with the capacity to remove unusableepisodes, and realize heartbeat-level diagnosis with confidence provided.

With the rapid development of wireless communication technologies, Internet of Things (IoT) has emerged as one of the most important application scenarios. Due to the high density of IoT devices and the limited spectrum resources, along with the miniaturization and sustainability requirements of these devices, the development of low-cost interference management (IM) methods has become crucial for widespread use of IoT. Interference has long been known to harm network performance. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we proposeInterference ReCycling(IRC) for the IoT. Under IRC, a recycling signal is generated using the interference a victim IoT device is subjected to, and then sent by the device’s associated gateway. Under the influence of the recycling signal, the desired data of the interfered/victim IoT transmission-pair can be recovered from the interference at the IoT device. We also show that the interfered user’s spectral efficiency (SE) with IRC can be optimized further by properly distributing the transmit power used for the desired signal’s transmission and the recycling signal. We validate the feasibility of IRC by implementing the method on the Universal Software Radio Peripheral (USRP) platform. Our theoretical analysis, experimental and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the SE of the victim IoT device compared to other existing IM methods.

Datasets often include noisy labels, but learning from them is difficult. Since mislabeled examples usually have larger loss values in training, the small-loss trick is regarded as a standard metric to identify the clean example from the training set for better performance. Nonetheless, this proposal ignores that some clean but hard-to-learn examples also generate large losses. They could be misidentified by this criterion. In this paper, we propose a new metric called the Integrated Area Margin (IAM), which is superior to the traditional small-loss trick, particularly in recognizing the clean but hard-to-learn examples. According to the IAM, we further offer the Hyperspherical Margin Weighting (HMW) approach. It is a new sample weighting strategy that restructures the importance of each example. It should be highlighted that our approach is universal and can strengthen various methods in this field. Experiments on both benchmark and real-world datasets indicate that our HMW outperforms many state-of-the-art approaches in learning with noisy label tasks. Codes are available at https://github.com/Zhangshuojackpot/HMW.

With the increasing number of wireless devices connecting to networks and sharing the same spectrum resources, interference has become a significant obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and mainly focus on suppressing or eliminating its impact on desired transmissions. However, this often comes at the cost of consuming communication resources. Therefore, design of low-cost IM method that can exploit interference is of research importance. To achieve this goal, we leverage the cost-effectiveness and adaptable deployment capabilities of Intelligent Reflecting Surface (IRS) to propose an IRS Empowered Interference Utilization (IRS-IU) method to realize efficient desired data transmission. By appropriately designing the reflection coefficient of the IRS, a phase shift is introduced to the incident interference, allowing the reflecting interference to interact with its direct counterpart at the interfered receiver (Rx). As a result, the interfered Rx can retrieve its desired data from the mixed interference. In this way, IRS-IU can make full use of the interference to enhance the desired data transmission. Our theoretical analysis and simulation results show that the proposed method can significantly improve the spectral efficiency (SE) of the interfered communication-pair.

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective and can offer extra strength to resist mislabeled samples. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under some conditions.

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

With the increasing number of wireless devices connecting to networks and sharing spectrum resources, interference has become a major obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and focus on suppressing or eliminating its impact on the transmission of intended signals. However, this often comes at the cost of consuming communication resources and degrading desired transmission performance. Therefore, the design of a cost-effective IM method that “exploits” interference is important. To achieve this goal, we proposeIntelligent Reflecting Surface Assisted Interference Exploitation(IRS-IE) to realize efficient desired data transmission. IRS-IE leverages the low-cost and adaptive deployment capabilities of IRS to gather and reflect interference towards the interfered receiver (Rx). By appropriately designing the reflection coefficient of IRS, a phase shift is introduced to the incident interference, allowing the reflected interference to interact with its direct counterpart at the interfered Rx. As a result, the interfered Rx can retrieve its desired data from the mixed interference. This way, IRS-IE can make use of the interference to facilitate the desired data transmission. Our theoretical analysis and simulation results show that IRS-IE significantly improves the spectral efficiency (SE) of the interfered communication pair over the other IM methods.

In this research, we propose a variant of the Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for hyper-parameter selection and deep architecture generation for image, audio and video classification tasks. Since the search process of the original BBPSO model is guided by a single leader and the particles’ personal best experiences, there is a lack of interactions pertaining to the neighbouring elite solutions. To overcome this limitation, we propose a versatile search process for a modified BBPSO model that incorporates a number of effective components and operations. These include the neighbouring and global best signals, search actions with Cauchy/Levy scale factors, sub-dimension operations guided by the local and global elite solutions, and a Levy-driven local search mechanism. Moreover, root-finding algorithms are employed which use informative mathematical principles to estimate new root offspring for leader/particle enhancement. A reinforcement learning algorithm is subsequently used to identify the optimal sequential deployment of these numerical analysis methods to increase robustness. Several medical imaging data sets, i.e., ISIC 2017, PH2 and Dermofit skin lesion databases, the ALL-IDB2 microscopic blood image data set, the MURA musculoskeletal radiographic database, the CK + facial expression data set, as well as the Coswara respiratory audio data set and UCF101 video action data set, are employed for evaluation. The proposed BBPSO-optimized Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM) with attention mechanism, and CNN-BiLSTM models outperform those devised by other PSO and BBPSO variants, as well as state-of-the-art existing studies, significantly, for image, audio respiratory abnormality and realistic video action recognition.

Electrocardiogram (ECG) is one of the most important information for cardiovascular diseases (CVDs) diagnosis. In recent year, several dry electrode-based smart clothes have been widely developed to improve the skin allergic reaction and gel-drying issue from conventional Ag/AgCl electrode under long-term measurement. However, most of these dry electrodes still have to contact with skin and may encounter the risk of skin irritation, and many smart clothing systems lack of automatic CVDs detection. In this article, a novel smart clothing was designed to automatically detect CVDs in daily life. Based on the technique of capacitive electrodes, the proposed smart clothing could access the bio-potential across the clothes to prevent the skin from irritation and discomfort, and could adapt to different body sizes by the specific belt mechanical design. Moreover, the CVDs detection algorithm was also designed and implemented in the field programmable gate array (FPGA) based ECG analysis module. The experiment results show that the proposed smart clothing could effectively real-time extract ECG features (P-, R-, and T-waves) and detect CVDs state via the front-end circuit, including bradycardia, tachycardia, atrial fibrillation, left ventricular hypertrophy, first-degree atrioventricular block, and hyperkalemia. The proposed FPGA architecture is also beneficial for future revisions or additions of CVD algorithms to improve more accurate diagnosis and monitoring of heart disease. It might reduce huge ECG data collected in daily life via only transmitting the abnormal ECG segment, and improve the diagnostic efficiency of CVDs in the future.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology ($A_{m}$) and rhythm ($A_{r}$). Our key insight is to suppress the over-dependence on$A_{m}$or$A_{r}$while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on$A_{m}$and$A_{r}$, respectively. In this article, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.

Sample Entropy (SampEn) is an information en-tropy algorithm widely used for complexity analysis and chaos estimation in many applications. In particular, SampEn measures complexity of time series by the conditional probability of the inner pattern. Unfortunately, the straightforward implementation of SampEn is quadratic time complexity, restricting its real-time analysis ability for health applications and long-term data analysis. Although researchers have proposed fast versions of SampEn to avoid unnecessary comparisons, they have not been accelerated yet due to their performance bottleneck in the complex similarity pair process. In this paper, we evaluate fast SampEn algorithms by employing multi-source biomedical signals on an Field-Programmable Gate Arrays (FPGA). Since fast SampEn algorithms based of a pre-sorting stage promise to outperform other SampEn algorithms, Lightweight SampEn based on Merge Sort is here implemented and optimized. Dif-ferent type of optimizations, that can be generalized for similar Lightweight-based SampEn algorithms, are used to reduce the overall latency while the data throughput is increased. A load balancing strategy for multi similarity pair modules is also proposed to solve the unbalancing loads, a bottleneck when increasing the execution parallelism of this type of algorithms. As a result, the proposed SampEn architecture runs 10 times faster than the fastest SampEn implementation on a modern CPU.

The analysis of facial expression is a very complex and challenging problem. Most researches for automated Facial Expression Recognition (FER) are mainly based on deep learning networks, rarely considering data imbalance. This paper commits to addressing the long-tail distribution problems among large-scale datasets in wild. Inspired by the continual learning method, we reconstruct multi-subsets first by randomly selecting from head classes and up-sampling tail classes. A pre-trained backbone is then introduced to learn general weights in a repeatedly train-prune fashion. Hereafter, our approach creatively trains a new classifier based on union parameters previously preserved and achieves an outperformance without extra parameters added in, using the gradual-prune technique. The results show that the independent training of classifiers has been a contributing factor. We successfully conduct this experiment with several classic networks, prove its effectiveness in training a deep network on imbalanced dataset. In the face of the poor performance in current FER, we find that domain knowledge is somehow affecting the accuracy of recognition by further exploring the obstacles from the image itself.Code available at https://github.com/Epicghx/FER

In this article, we propose an attention based convolutional neural network long short-term memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data, i.e., acceleration and heart rate variability (HRV). Since the three-dimensional acceleration data was sampled with a high frequency, we firstly design a CNN-LSTM structure to effectively learn latent features from the acceleration. Meanwhile, considering the unique format of the HRV data, some effective features are extracted based on domain knowledge. Next, we design a unified architecture to efficiently merge the features learned by CNN-LSTM approach from the acceleration and the extracted features from the HRV, which enables us to make full use of all the available information from these two heterogeneous sources. Taking into consideration that these two heterogeneous sources may have distinct contributions for the sleep and wake states, we propose an attention network to dynamically adjust the importance of features from the two sources. Real-world experiments have been conducted to verify the effectiveness of the proposed approach for sleep-wake detection. The results demonstrate that the proposed method outperforms all existing approaches for sleep-wake classification. In the evaluation of leave-one-subject-out (LOSO) cross-validation which is more challenging and practical, the proposed method achieves remarkable improvements ranging from 5% to 46% over the benchmark approaches.

Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorithm may break down on certain subjects, resulting in a significant estimation error. Ensemble regression, which aggregates the outputs of multiple base estimators for more reliable and stable estimates, can be used to remedy this problem. Moreover, active learning can be used to optimally select a few trials from a new subject to label, based on which a stacking ensemble regression model can be trained to aggregate the base estimators. This paper proposes four active stacking approaches, and demonstrates that they all significantly outperform three common unsupervised ensemble regression approaches, and a supervised stacking approach which randomly selects some trials to label. Remarkably, our active stacking approaches only need three or four labeled trials from each subject to achieve an average root mean squared estimation error below three beats per minute, making them very convenient for real-world applications. To our knowledge, this is the first research on active stacking, and its application to heart rate estimation.

Abstract This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.

Recently, development of wearable and Internet of Things (IoT) technologies enables the real-time and continuous individual electrocardiogram (ECG) monitoring. In this paper, we develop a novel IoT-based wearable 12-lead ECG SmartVest system for early detection of cardiovascular diseases, which consists of four typical IoT components: 1) sensing layer using textile dry ECG electrode; 2) network layer utilizing Bluetooth, WiFi, etc.; 3) cloud saving and calculation platform and server; and 4) application layer for signal analysis and decision making. We focus on addressing the challenge of real-time signal quality assessment (SQA) and lightweight QRS detection for wearable ECG application. First, a combination method of multiple signal quality indices and machine learning is proposed for classifying 10-s single-channel ECG segments as acceptable and unacceptable. Then a lightweight QRS detector is developed for accurate location of QRS complexes. The results show that the proposed SQA method can efficiently deal with tradeoff between accepting good (97.9%) and rejecting poor (96.4%) quality ECGs, ensuring that only a low percentage of recorded ECGs are discarded. The proposed lightweight QRS detector achieves a${F_{1}}$score higher than 99.5% for processing clean ECGs. Meanwhile, it reports significantly higher${F_{1}}$scores than two existing QRS detectors for processing noisy ECGs. In addition, it also has a fine computation efficiency. This paper demonstrates that the developed IoT-driven ECG SmartVest system can be applied for widely monitoring the population during daily life and has a promising application future.

Speech is easily affected by different background noise in real environment to reduce the speech intelligibility, in particular, for hearing impaired listeners. In order to improve the above issue, several hearing aids have been developed to enhance the speech signal in noisy environment. Most of current hearing aids were designed to enhance the component of speech and suppress the component of noise. However, it is difficult to separate other speech sources. Adaptive signal enhancement with the beamforming technique might improve the above issue. However, how to distinguish the location of the desired speaker effectively is still a difficult challenge for adaptive beamforming method. A novel concept of hearing aid was proposed in this study. Different from the beamforming-based hearing aids, which use the crosscorrelation-coefficient method to estimate time difference of arrival (TDOA), an image recognition technology was used to estimate the location of the desired speaker to obtain the more precise TDOA. An adaptive signal enhancement was also used to enhance the noisy speech sound. From the experimental results, the proposed system could provide a smaller absolute error of TDOA less than 1.25 × 10-4ms, and a clear speech sound from the target speaker who the user wants to listen to.

The scalability of medical technology in low resource settings requires a higher level of usability and clear decision support compared to conventional devices, since users often have very limited training. In particular, it is important to provide users with real time feedback on data quality during the patient information acquisition in a manner that enables the user to take immediate corrective action.

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heartbeats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1 s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as "Korotkoff." A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analyzed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analyzed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analyzed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at sixth beat below SBP) and DBP (98.1%, at sixth beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high-cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject.

We propose a systematic ECG quality classification method based on a kernel support vector machine (KSVM) and genetic algorithm (GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function (GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function (MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search (GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive (TP), false positive (FP), and classification accuracy were used as the assessment indices. For training database set A (1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were: TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B (500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.