What question did this study set out to answer?

To classify different types of cardiac arrhythmias using deep learning algorithms.

May 4, 2018Open Access

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

SSShalin SavaliaSt. Mary's University, Texas VEVahid EmamianSt. Mary's University, Texas

Key Points

To classify different types of cardiac arrhythmias using deep learning algorithms.
Developed a multi-layer perceptron (MLP) and convolution neural network (CNN) using TensorFlow.

Structured PICO

Do multi-layer perceptron and convolution neural network algorithms improve the accuracy of cardiac arrhythmia classification from ECG signals?

Population

ECG databases accessible at PhysioBank.com and kaggle.com

Intervention

Multi-layer perceptron (MLP) and convolution neural network (CNN) algorithms

Comparator

Current algorithms developed by other cardiologists

Outcome

Accuracy, sensitivity, and precision of arrhythmia classification

Deep learning algorithms using MLP and CNN demonstrate superior sensitivity and precision for classifying cardiac arrhythmias from ECG signals compared to existing methods.

Abstract

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

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