Remote R-peak Detection in Single-Lead ECG Chest Monitors Using Clustering Classification and Laser-Induced Graphene Electrodes

This study presents a complete system for remote single-lead electrocardiogram (ECG) monitoring using a wearable device equipped with dry laser-induced graphene (LIG) electrodes. First, we evaluated the feasibility of LIG electrodes for chest ECG acquisition across different volunteers and over extended recording periods. After that, a simple algorithm for R-peak detection was developed and validated using the acquired signals. This algorithm implemented using open-source libraries in Python, consists of two optimized stages. In the first stage, the ECG signal is preprocessed using the discrete wavelet transform (DWT) to remove the baseline wandering and denoise the signal. Secondly, a clustering-based algorithm classifies the different peaks in the ECG signal into two clusters based on their amplitude range. It is demonstrated that, despite the inter-individual variability in ECG waveforms, the proposed algorithm effectively identifies the R-peaks with high accuracy without requiring the specific cardiac profile for each individual patient, thus ensuring a reliable and robust heart rate (HR) monitoring. Finally, we showcase the potential of integrating novel wearable ECG monitoring systems with advanced sensors and signal processing techniques by a near real-time remote ECG monitoring setup.