Median-based abnormal heartbeat detection using histogram for arrhythmia reading

Abstract

An abnormal heartbeat caused by the occurrence of arrhythmia in an electrocardiogram signal is very important for arrhyth-mia reading, and studies on detecting abnormal heartbeats are ongoing. The median absolute deviation method, which is a representa-tive method for detecting abnormal heartbeats, uses the insensitivity of the median value and the median absolute deviation in the feature distribution of the abnormal heartbeat. However, this has a disadvantage in that the processing time is long because the features must be sorted to obtain the median value and median absolute deviation. To improve this, we propose a median-based abnormal heartbeat detection method using a histogram. Unlike the existing median absolute deviation method, the proposed method does not use a sorting process but rather finds the median value and median absolute deviation of the QRS complex’s features, which are shape and interval features of the heartbeat, and detects abnormal heartbeats using the standard deviation method. MIT-BIH ADB, a repre-sentative arrhythmia database, was used to evaluate the performance of the proposed method. We confirmed that the proposed method reduced the processing time by approximately 81.79% compared with the existing median absolute deviation method and that the efficiency of the proposed method increases as the amount of heartbeat data increases. In addition, in the proposed method, the non-detection rate of an abnormal heartbeat was 0%, and the over-detection rate of a normal heartbeat was found to be 1.8% on average.