Development of a Wearable Biosensor System for Monitoring Cardiac Physiological Signals

Thesis Abstract

Cardiovascular diseases remain the leading cause of mortality worldwide, necessitating innovative solutions for continuous, real-time monitoring of cardiac physiological signals to enable early detection and intervention. Despite advancements in telemedicine and diagnostic tools, current monitoring devices are often limited by their lack of portability, accuracy, and user-friendliness, which hinders widespread adoption for daily health management. This study aims to develop and validate a wearable biosensor system capable of reliably capturing and analyzing cardiac signals such as electrocardiograms (ECG), heart rate variability (HRV), and arrhythmia detection to improve patient monitoring and clinical decision-making. The specific objectives include designing a compact, low-power wearable biosensor integrated with signal processing and data transmission capabilities, evaluating its performance stability and accuracy against clinical-grade ECG systems within a defined population, and examining its usability through user acceptance testing. The research intends to address the following questions How accurately can the biosensor system measure cardiac signals in real-world conditions? What is the system’s reliability and consistency during prolonged wear? And, how acceptable is the device to users across different demographic groups? The study employs a quantitative research paradigm characterized by a cross-sectional, experimental design. The population comprises 150 adults aged 25-65 years, recruited through stratified random sampling from a cardiovascular outpatient clinic in a metropolitan setting. Data collection involves deploying the developed biosensor device alongside standard 12-lead ECGs to collect comparative cardiac signal data over a period of four weeks. Participants will wear the biosensor during daily activities to assess performance under typical conditions. Signal data will be processed and analyzed through statistical techniques such as Bland-Altman analysis for agreement assessment, regression analysis for predictive validity, and intra-class correlation coefficients to evaluate reliability. Additionally, usability and acceptance data will be analyzed via descriptive and inferential statistics, including chi-square tests and t-tests. The anticipated findings suggest that the biosensor system will demonstrate high concordance with clinical ECG standards, with a sensitivity and specificity exceeding 90% in detecting arrhythmic events and abnormal HRV metrics. The device is expected to exhibit consistent signal quality across different users and activity levels, with minimal signal artifacts. Findings from user feedback are projected to indicate strong usability and acceptance among diverse demographic groups, emphasizing its potential for widespread adoption in personal health management. This research significantly contributes to the field of wearable health technologies by providing evidence-based insights into the design, validation, and user acceptability of portable cardiac monitoring systems. It addresses existing gaps related to device accuracy, reliability, and user engagement, thereby advancing the development of personalized telecardiology solutions. The validation framework and the integrated biosensor architecture proposed herein extend current knowledge on wearable physiological monitoring, offering a scalable model adaptable to various clinical and non-clinical settings. In conclusion, the study advocates for the incorporation of the developed biosensor system into routine healthcare practices to facilitate early detection of cardiovascular anomalies and support remote health monitoring. Recommendations emphasize further longitudinal assessments, integration with mobile health applications, and exploration of machine learning algorithms for enhanced signal analysis. Future research should explore miniaturization, energy harvesting solutions to extend device lifespan, and the potential for integration with electronic health records to support comprehensive cardiovascular care.