Abstract:
Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances in the heartbeat caused
by electrical conduction anomalies in cardiac muscle. Clinically, ECG machines are utilized to diagnose and monitor
cardiac arrhythmia noninvasively. Since ECG signals are dynamic in nature and depict various complex information,
visual assessment and analysis are time consuming and very difcult. Therefore, an automated system that can assist
physicians in the easy detection of arrhythmia is needed.
Method The main objective of this study was to create an automated deep learning model capable of accurately
classifying ECG signals into three categories: cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal
sinus rhythm (NSR). To achieve this, ECG data from the MIT-BIH and BIDMC databases available on PhysioNet were
preprocessed and segmented before being utilized for deep learning model training. Pretrained models, ResNet 50
and AlexNet, were fne-tuned and confgured to achieve optimal classifcation results. The main outcome measures
for evaluating the performance of the model were F-measure, recall, precision, sensitivity, specifcity, and accuracy,
obtained from a multi-class confusion matrix.
Result The proposed deep learning model showed overall classifcation accuracy of 99.2%, average sensitivity
of 99.2%, average specifcity of 99.6%, average recall, precision and F- measure of 99.2% of test data.
Conclusion The proposed work introduced a robust approach for the classifcation of arrhythmias in comparison
with the most recent state of the art and will reduce the diagnosis time and error that occurs in the visual investiga tion of ECG signals.
