Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection

Flexible Thermoelectric Wearable Architecture for Wireless Continuous Physiological Monitoring

Continuous monitoring of physiological signals from the human body is critical in health monitoring, disease diagnosis, and therapeutics. Despite the needs, the existing wearable medical devices rely on either bulky wired systems or battery-powered devices needing frequent recharging. Here, we introduce a wearable, self-powered, thermoelectric flexible system architecture for wireless portable monitoring of physiological signals without recharging batteries. This system harvests an exceptionally high open circuit voltage of 175-180 mV from the human body, powering the wireless wearable bioelectronics to detect electrophysiological signals on the skin continuously. The thermoelectric system shows long-term stability in performance for 7 days with stable power management. Integrating screen printing, laser micromachining, and soft packaging technologies enables a multilayered, soft, wearable device to be mounted on any body part. The demonstration of the self-sustainable wearable system for detecting electromyograms and electrocardiograms captures the potential of the platform technology to offer various opportunities for continuous monitoring of biosignals, remote health monitoring, and automated disease diagnosis.

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Soft wireless sternal patch to detect systemic vasoconstriction using photoplethysmography

Vasoconstriction is a crucial physiological process that serves as the body's primary blood pressure regulation mechanism and a key marker of numerous harmful health conditions. The ability to detect vasoconstriction in real time would be crucial for detecting blood pressure, identifying sympathetic arousals, characterizing patient wellbeing, detecting sickle cell anemia attacks early, and identifying complications caused by hypertension medications. However, vasoconstriction manifests weakly in traditional photoplethysmogram (PPG) measurement locations, like the finger, toe, and ear. Here, we report a wireless, fully integrated, soft sternal patch to capture PPG signals from the sternum, an anatomical region that exhibits a robust vasoconstrictive response. With healthy controls, the device is highly capable of detecting vasoconstriction induced endogenously and exogenously. Furthermore, in overnight trials with patients with sleep apnea, the device shows a high agreement (r² = 0.74) in vasoconstriction detection with a commercial system, demonstrating its potential use in portable, continuous, long-term vasoconstriction monitoring.