Segment Based Pattern Analysis Reveals a Persistent Regular Rhythm in the Motion Artifact Record of the MIT-BIH Noise Stress Test Database

Abstract

AbstractBackgroundThe MIT-BIH Noise Stress Test Database (NSTDB) is a publicly available resource for testing QRS detection algorithms. Serial QRS detection algorithms applied to the NSTDB have apparently failed to detect the presence of a possible heartbeat like rhythm associated with peaks that the NSTDB classifies as noise. The failure to detect this rhythm may arise from the difficulty associated with interpreting noisy RR interval time series produced by serial QRS detection schemes.Algorithm Summary and ExperimentTo extract rhythm information from noisy peak time/RR interval time series, a peak space signal is created with triangular pulses centered on peaks located by a serial QRS detection algorithm such as Pan-Tompkins. The peak space signal is autocorrelated over 20 s segments and the primary (non-origin) peak in the autocorrelation signal is located. In the presence of reasonably regular sinus rhythm, this peak corresponds to a fundamental RR interval present throughout the 20 s segment. This peak time processing method was applied to the Pan-Tompkins QRS detections in the motion artifact record of the NSTDB. To compare the results to a different algorithm capable of detecting patterns at the segment level, a previously described pattern-based heartbeat detection scheme (Temporal Pattern Search, or “TEPS”) was applied in both single and multiple channel modes to the NSTDB motion artifact record.ResultsBoth the Pan-Tompkins/autocorrelation method and TEPS detected a persistent rhythm around 1000-1050 ms in both channels throughout the entirety of the motion artifact record. The RR interval correlation between Pan-Tompkins/autocorrelation and single channel TEPS was 0.8 and 0.7 in channels 1 and 2 respectively with p values of 0.