  Using smart wearable devices to monitor patients electrocardiogram (ECG) for
real-time detection of arrhythmias can significantly improve healthcare
outcomes. Convolutional neural network (CNN) based deep learning has been used
successfully to detect anomalous beats in ECG. However, the computational
complexity of existing CNN models prohibits them from being implemented in
low-powered edge devices. Usually, such models are complex with lots of model
parameters which results in large number of computations, memory, and power
usage in edge devices. Network pruning techniques can reduce model complexity
at the expense of performance in CNN models. This paper presents a novel
multistage pruning technique that reduces CNN model complexity with negligible
loss in performance compared to existing pruning techniques. An existing CNN
model for ECG classification is used as a baseline reference. At 60% sparsity,
the proposed technique achieves 97.7% accuracy and an F1 score of 93.59% for
ECG classification tasks. This is an improvement of 3.3% and 9% for accuracy
and F1 Score respectively, compared to traditional pruning with fine-tuning
approach. Compared to the baseline model, we also achieve a 60.4% decrease in
run-time complexity.
