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.

Entity alignment has emerged as a powerful technique for integrating knowledge graphs, facilitating the fusion of heterogeneous knowledge into a unified graph. The state-of-the-art methods combine both graph structures and side information for effective entity alignment. However, they neglect low-quality issues in data. Specifically, the emerging knowledge graphs in diverse fields amass a wealth of entities that lack not only adequate descriptions but also annotated alignments. These two limitations lead to the overfitting problem and degrade the alignment performance. To tackle these challenges, we propose DEBT, an innovative approach that systematically enhances entity alignment. It first enriches the descriptions of entities by aggregating their neighbors and attributes. Then, a bootstrap strategy is utilized to expand the training set by incorporating entity pairs with similarity scores exceeding a dynamically decreasing threshold. Experimental results demonstrate that our method achieves the state-of-the-art accuracy while reducing the number of annotated entity alignment pairs.

The performance of computer vision models in certain real-world applications, such as medical diagnosis, is often limited by the scarcity of available images. Expanding datasets using pre-trained generative models is an effective solution. However, due to the uncontrollable generation process and the ambiguity of natural language, noisy images may be generated. Re-weighting is an effective way to address this issue by assigning low weights to such noisy images. We first theoretically analyze three types of supervision for the generated images. Based on the theoretical analysis, we develop TriReWeight, a triplet-connection-based sample re-weighting method to enhance generative data augmentation. Theoretically, TriReWeight can be integrated with any generative data augmentation methods and never downgrade their performance. Moreover, its generalization approaches the optimal in the order O(√d ln (n)/n). Our experiments validate the correctness of the theoretical analysis and demonstrate that our method outperforms the existing SOTA methods by 7.9% on average over six natural image datasets and by 3.4% on average over three medical datasets. We also experimentally validate that our method can enhance the performance of different generative data augmentation methods.

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.

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.

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.

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.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans.