Efficiency of Different Heartbeat Detection Methods by Using Alternative Noise Reduction Algorithms

Abstract

Motivation: ECG noise reduction is an essential step in the ECG preprocessing pipeline. In particular, the quality of beat detection can be affected by several artifacts. The variety of available methods ranges from filter-based techniques (e.g. Butterworth or FIR), signal decomposition (e.g. wavelets) to neural networks (Cycle-GAN). Methods: 12-lead resting ECGs from the SFB/TR19 study on inflammatory dilated cardiomyopathy (n=704) and from the Study of Health in Pomerania (n=17,7l7) were preprocessed with 14 different methods to evaluate the accuracy of heartbeat detection methods in relation to the chosen method for preprocessing. Open source signal libraries (neurokit2, py-ecg-detectors, WFDB among others) with 34 detectors were evaluated. Sensitivity and Positive Predictive Values (PPV) were computed for each combination of preprocessing and detection method in a train/test scheme. Annotations were corrected for fixed delays and scored at a tolerance of 50 ms. Results and conclusion: Eplimited performed best, regardless of the chosen preprocessing method. For diseased ECGs, in Kalidas2017 it was seen an improvement in performance from 0.713 to 0.876 and from 0.778 to 0.879 for PPV and sensitivity respectively. Best results can be achieved with ECG leads V3, V5 and V6.