Model Compression Techniques for Atrial Fibrillation Detection on Mobile Devices

Deep Neural Networks (DNN) for resource-constrained mobile devices tend to reduce diagnosis latency and alleviate privacy concerns of the patients in the healthcare domain. Since mobile devices are limited by computational power and battery capacity, traditional DNN requires optimization. This paper analyzes the effect of compression techniques to detect Atrial Fibrillation (AFib) from Electrocardiogram signals. Specifically, we design a framework that compresses Residual Neural Network (ResNet) and Long Short Term Memory (LSTM) model using Weight Pruning, Weight sharing, Quantization, Huffman encoding, and Tensorflow Lite for AFib detection in android devices. The compressed models are evaluated on PhysioNet Computing in Cardiology Challenge 2017 database, and models are evaluated using size, inference time, precision, accuracy, sensitivity, and specificity. The models are tested on desktop computer and android smartphone.