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.

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

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.

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.