ECG Disease Classification Using 1D CNN

Recent improvements in the Artificial Intelligence and Machine Learning lead us to the automated models for detecting diseases based on ECGs. The previous models based on 2D CNNs had pretty good accuracy in ECG Classification, but these models requires us to create a 2D spatial representation from ECGs as ECGs are 1D temporal data. As these are typically done through CWT Scalogram it is computationally intensive. In this paper a model based on a 1D Convolutional Neural Network is proposed which can classify specific cardiac disease from ECG data. This model consists of four feature extracting Convolutional layers each succeeded by a Max Pooling layer and a dropout layer to prevent overfitting. This model is trained with ECGs of different types based on different cardiac disease like Cardiac Arrhythmia, Congestive Heart Failure and Normal Sinus Rhythm etc. and it can classify these diseases from ECGs. For training and evaluation MIT-BIH arrhythmia database have been used. It results in classification accuracy of 96.83% which is almost equal to the previous 2D CNN model with significant decrease in complexity.