A Low-Complexity R-peak Detection Algorithm with Adaptive Thresholding for Wearable Devices

Abstract

A reliable detection of the R-peaks in an electrocardiogram (ECG) is a fundamental step for further heart rate variability (HRV) analysis, biometric recognition techniques and additional ECG waveform based analysis. In this paper, a novel real-time and low-complexity R-peak detection algorithm is presented for single lead ECG signals. The detection algorithm is divided in two stages. In the first pre-processing stage, the QRS complex is enhanced by taking the double derivative, squaring and moving window integration. In the second, the detection of the R-peak is achieved based on a finite state machine (FSM) approach. The detection threshold is dynamically adapted and follows an exponential decay after each detection, making it suitable for R-peak detection under fast heart rate (HR) and R-wave amplitude changes without additional search back. The proposed algorithm was evaluated in a private single lead ECG database acquired using a FieldWiz wearable device. The database comprises five recordings from four different subjects, recorded during dynamic conditions, namely: running, trail running and weightlifting. The raw ECG signals were annotated for the R-peak and the proposed method benchmarked against common QRS detectors. The combined acquisition setup and presented approach resulted in R-peak detection Sensitivity (Se) of 99.77% and Positive Predictive Value of (PPV) of 99.18%, comparable to state of the art real time QRS detectors. Due to its low computational complexity, this method can be implemented in embedded wearable systems, suited for cardiovascular tracking devices in dynamic use cases and R-peak detection.