ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
This work addresses the problem of accurate ECG arrhythmia classification for medical diagnostics, using an incremental approach that adapts existing image-based deep learning methods to a new domain.
The paper tackled the challenge of classifying ECG arrhythmias with limited training data by applying transfer learning from a deep CNN trained on generic images to ECG spectrograms, achieving 97.23% accuracy on nearly 7000 instances.
Due to the recent advances in the area of deep learning, it has been demonstrated that a deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias better than cardiologists. Moreover, traditionally feature extraction was considered an integral part of ECG pattern recognition; however, recent findings have shown that deep neural networks can carry out the task of feature extraction directly from the data itself. In order to use deep neural networks for their accuracy and feature extraction, high volume of training data is required, which in the case of independent studies is not pragmatic. To arise to this challenge, in this work, the identification and classification of four ECG patterns are studied from a transfer learning perspective, transferring knowledge learned from the image classification domain to the ECG signal classification domain. It is demonstrated that feature maps learned in a deep neural network trained on great amounts of generic input images can be used as general descriptors for the ECG signal spectrograms and result in features that enable classification of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in classifying near 7000 instances by ten-fold cross validation.