A Multimodal Data Fusion Technique for Heartbeat Detection in Wearable IoT Sensors

Abstract

The accurate detection of heartbeats is of paramount importance in the current healthcare scenario as they act as an indicator for various underlying cardiac conditions and provides an indication of cardiorespiratory fitness. The article presents a novel multimodal data fusion technique using the discrete wavelet transform (DWT) and an application for fusing electrocardiogram (ECG), and photoplethysmogram (PPG) signals to improve beat detection accuracy in ambulatory monitoring using Internet of Things (IoT) sensors. The characteristics of interest from the input signals are first isolated in the wavelet domain and then combined to form a fused feature signal using a weighted average. The weights used are derived from a signal quality index calculation algorithm, suitable for periodic/quasiperiodic signals of different wave morphologies. The peak detection process to identify the heartbeat locations is carried out on the final fused signal. The research evaluates the algorithm performance when different types of noises at varying amplitudes corrupt the ECG and PPG signal inputs, affecting the signal-to-noise ratios (SNRs). The algorithm consistently exhibited a sensitivity of 99.69%, positive predictive value (PPV) of 99.64%, mean beat-to-beat interval relative error of 0.01, and an error spread (corresponding to 90th percentile of relative errors) of 0.02 in the −30 to 50 dB SNR range for all noise scenarios considered. The proposed algorithm exhibits improved detection sensitivities and PPVs under ambulatory conditions compared to state-of-the-art beat detection algorithms and can be used to accurately detect heartbeats where single-channel monitoring tends to fail in IoT devices.