Comprehensive Assessment of a Multi-Channel Physiological Sensor Platform for Real-Time Cardiac Health Monitoring

Real-time cardiac health monitoring is essential for early diagnosis and management of heart conditions. Multi-channel physiological sensors, integrating ECG, MCG, and other signals, offer improved accuracy. However, detailed evaluations using advanced statistical approaches are limited. The goal is to evaluate the performance of a multi-channel physiological sensor platform for real-time cardiac health monitoring, focusing on signal accuracy, reliability, and usability. While Structural Equation Modeling (SEM) explores relationships between factors influencing performance. A wearable sensor platform combining ECG and MCG technologies was tested with 78 participants under varying activity levels (rest, exercise) and environmental conditions. SEM, Exploratory Factor Analysis (EFA) was applied to model connections between sensor placement (SP), signal type (ST), activity level (AL), and system performance (SPF). Assesses the use of linear regression models to forecast and monitor cardiac health metrics using sensor data. SEM revealed significant links between sensor placement, environmental factors, and signal quality, explaining 75% of the variance in performance. The platform demonstrated reliable real-time monitoring with accuracy comparable to clinical standards. Sensor placement and environmental conditions were identified as key factors influencing performance, offering pathways for further optimization. This analysis enhances the potential for improved cardiac health monitoring and early intervention.