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A Deep Learning System for the Diagnosis of Heart Problems from ECG Media Files

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Heart diseases are a major illness worldwide. There is a need for an accurate and reliable diagnosis procedure, which should not put heavy burden on the already overwhelmed medical staff, always available, and easily accessible, for people with high risk of heart diseases. Machine learning has the ability to learn from large amounts of data, and it may offer accurate and reliable diagnosis of new data. In this paper, two convolutional neural network (CCN) architectures have been evaluated, i.e AlexNet and GoogleNet, in order to help diagnosing four heart conditions: Arrhythmia (ARR), Atrial Fibrillation (AF), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). A dataset of Electrocardiogram (ECG) Media files for heart related problems is fed into a deep learning (CNN) module to learn features and link them to corresponding labels. Results have showed that this technique is promising and could provide reliable solution to quick and reliable diagnosis of heart conditions, with an accuracy of 97.6%.
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CNN; Deep Learning; ARR; AF; CHF; NSR

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