A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals.

Objective Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features. Methods Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and "UNSW" R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database. Results For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% on the synthesized datasets with the signal-to-noise ratio (SNR) of 6 dB. With the same datasets, the Se of FB algorithm increased about 9.29% and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% on the synthesized one-heartbeat datasets. Conclusion Our method effectively improves the performance of cardiac application algorithms in noisy environments. It is the first study to utilize a DNN to capture noisy segments of the ECG signal. Significance Too many false alarms can cause alarm fatigue. Our method can be utilized as the pre-processing before signal analysis and thereby reducing false alarms from the ECG diagnostic systems.