Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management.

The Electrocardiogram (ECG) is a crucial tool for cardiac health monitoring, but existing ECG monitoring systems often lack effective signal conditioning coordination between hardware (HW) and software (SW), resulting in poor signal quality and monitoring effectiveness. To address this issue, this study presents a novel single-arm ECG monitoring system based on the Internet of Things that comprehensively considers both hardware and software aspects. The system mainly consists of an ECG measurement device and a signal reconstruction algorithm. For the device, impedance matching circuits and pre-amplifier circuits were specially designed to measure the weak single-arm ECG signal. Additionally, to improve wearing comfort, a metal dry electrode and an armband were developed. On the software side, an ECG signal reconstruction algorithm based on a diffusion model is proposed, and the reverse process of the diffusion model and the noise prediction model are improved according to the characteristics of the ECG signal. The feasibility of the system was verified through monitoring experiments conducted in real environments on healthy subjects and heart disease patients. The experimental results show that the proposed measurement device is capable of long-term ECG monitoring and that the signal reconstruction algorithm effectively removes various real-world noises. The R-wave detection precision, sensitivity, and F1-score of the reconstructed signal improved from 75.90% to 85.87%, 69.16% to 83.40%, and 72.37% to 84.62%, respectively. This demonstrates that the system provides a new solution for efficient cardiac monitoring in daily life. The open-source code is available at https://github.com/CHENJIAR3/ECG_Denoising/tree/Linfei.

Hypertension and blood pressure variability (BPV) are major risk factors for cardiovascular disease (CVD). Single-channel photoplethysmography (PPG) has emerged as a promising daily blood pressure (BP) monitoring tool. However, estimating BP trends presents challenges due to complex temporal dependencies and continuous fluctuations. Traditional methods often address BP prediction as isolated tasks and focus solely on temporal dependencies within a limited time window, which may fall short of capturing the intricate BP fluctuation patterns implied in varying time spans, particularly amidst constant BP variations. To address this, we propose a novel deep learning model featuring a two-stage architecture and a new input structure called contextual cycles. This model estimates beat-to-beat systolic blood pressure (SBP) trends as a sequence prediction task, transforming the output from a single SBP value into a sequence. In the first stage, parallel ResU Blocks are utilized to extract fine-grained features from each cycle. The generated feature vectors are then processed by Transformer layers with relative position encoding (RPE) to capture inter-cycle interactions and temporal dependencies in the second stage. Our proposed model demonstrates robust performance in beat-to-beat SBP trend estimation, achieving a mean absolute error (MAE) of 3.186 mmHg, a Pearson correlation coefficient applied to sequences (${\mathrm{R}}_{\mathrm{seq}}$) of 0.743, and a variability error (VE) of 1.199 mmHg. It excels in steady and abrupt substantial fluctuation states, outperforming baseline models. The results reveal that our method meets the requirements of the AAMI standard and achieves grade A according to the BHS standard. Overall, our proposed method shows significant potential for reliable daily health monitoring.

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.

Numerous psychological and clinical studies have confirmed a correlation between mental and cardiac health. We aim to explore this relationship further by examining how emotions influence cardiac health. By collecting body surface potential and utilizing the electrocardiographic imaging (ECGI) model, we can noninvasively and continuously reconstruct internal cardiac electrical activity. To enhance the existing ECGI model on various datasets, we propose an information fusion strategy called Emotional Potential Conversion CycleGAN. It enables data alignment across diverse datasets while preserving emotional information, allowing us to reconstruct cardiac electrical activity in various emotional states. Our results demonstrate successful data conversion while maintaining emotional integrity, achieving an impressive 91.92% accuracy in emotion recognition. We further validated this approach using publicly available datasets, WESAD and SWELL, which yielded consistent results. Additionally, we conducted preliminary investigations into the correlation and variability of cardiac activity across different sites under stress. The correlation study indicates a generalized association among various regions of the heart, while variability studies reveal that fluctuations in cardiac electrical activity during stress are primarily concentrated around the atrioventricular node and Purkinje fibers. This suggests a potential risk for pre-excitation syndrome, possibly due to the possible presence of a Kent bundle. Overall, we present a practical approach for studying the interplay between emotional states and cardiac health. Our findings indicate a potential relationship under stress that may provide valuable insights for future research.