Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Haemwaan Sivaraks,Chotirat Ann Ratanamahatana

doi:10.1155/2015/453214

Haemwaan Sivaraks, Chotirat Ann Ratanamahatana

Open Access

https://doi.org/10.1155/2015/453214

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Abstract

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

Highlights

Electrocardiogram (ECG or EKG) signal is a time series data sequence which represents electrical impulses from myocardium
To differentiate a real ECG beat from an ECG artifact, cardiologists need to compare the morphology of that beat to other ECG beats in cleaner leads based on the following facts: (1) each time alignment comes from the same electrical activity across every lead and (2) TP segment is an isoelectric interval on the ECG, which should always be at the baseline; that is, any changes in TP segment must be considered as ECG artifacts
accuracy on detection (AoD), sensitivity, and positive predictive value are not calculated since no anomaly beats are present

Summary

Introduction

Electrocardiogram (ECG or EKG) signal is a time series data sequence which represents electrical impulses from myocardium. Most physicians prefer a use of ECG as a noninvasive tool to detect and diagnose cardiac diseases. The two important characteristics of an ECG signal are its multiple signal recordings from various positions of myocardium and its periodic waveform [1] synchronized with a cardiac cycle. A normal ECG consists of five morphology segments, that is, PQRST waveforms that correspond to electrical conductivity through the whole cardiac cycle. ECGs from different leads may be morphologically different depending on the vector of the heart. The ECG morphology in each lead reflects the electrical activity in each segment of the heart. The multilead ECG could be used to interpret the electrical activity of the whole heart and is very useful for abnormal myocardium detection (for more details, see [2,3,4])

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