January 1, 2018Open Access

Classification of ECG Arrhythmia using Recurrent Neural Networks

Structured PICO

Do Recurrent Neural Networks accurately classify normal and abnormal beats in ECG time-series data?

Population

ECG time-series data from the MIT-BIH Arrhythmia database

Intervention

Recurrent Neural Networks (Long Short Term Memory Network)

Comparator

Different RNN models

Outcome

Classification performance (accuracy) of normal vs abnormal beats

LSTM networks demonstrate effectiveness in automatically classifying normal and abnormal ECG beats using the MIT-BIH Arrhythmia database.

Abstract

In this paper, Recurrent Neural Networks (RNN) have been applied for classifying the normal and abnormal beats in an ECG. The primary aim of this paper was to enable automatic separation of regular and irregular beats. The MIT-BIH Arrhythmia database is being used to classify the beat classification performance. The methodology used is carried out using huge volume of standard data i.e. ECG time-series data as inputs to Long Short Term Memory Network. We divided the dataset as training and testing sub-data. The effectiveness, accuracy and capabilities of our methodology ECG arrhythmia detection is demonstrated and quantitative comparisons with different RNN models have also been carried out.

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