High-Reliability Signal Quality Validation for Biosignals Using Sensor Fusion and Software Indices

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level and segment-level labels for real-time filtering and offline dataset curation. The framework is designed for non-stationary periodic biomedical time-series signals including electrocardiography (ECG), photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) and is demonstrated and evaluated primarily on ECG. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal-quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency-domain SNR estimation, and baseline-wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels demonstrates high performance, achieving approximately 98% accuracy, 98% F1-score, 99% sensitivity, and 97% specificity. This modular, extensible approach enhances the trustworthiness of downstream analytics by preventing contaminated segments from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems.