Lightweight Shufflenet Based CNN for Arrhythmia Classification

Recent advances in artificial intelligence (AI) and continuous monitoring of patients using wearable devices have enhanced the accuracy of diagnosing various arrhythmias, from the captured Electrocardiogram (ECG) signals. Achieving high accuracy when using Deep Neural Network (DNN) for ECG classification is accomplished at the cost of compute and memory intensive operations, thus limiting its deployment to devices with high computing capabilities, and makes it unsuitable for wearable edge devices. To facilitate the deployment of deep neural networks on wearable mobile edge devices with limited resources, a lightweight Convolution Neural Network (CNN) model based on the ShuffleNet architecture is proposed and implemented as a solution in this paper. A sliding window of variable stride is used to increase the number of under-represented classes in the database. Moreover, a novel encoding scheme is employed for labelling and training test set samples, allowing the model to detect multiple classes in one ECG segment. A loss function (Focal loss) that proved to be effective when applied for DNN training on an imbalanced dataset was also explored in this work. The proposed model outperformed traditional CNN with 9x less trainable parameters and improved the F1-score by 2%.

Automated summary

This study proposes a lightweight CNN model based on the ShuffleNet architecture for deploying deep neural networks on wearable mobile edge devices with limited resources. It utilizes a sliding window and novel encoding scheme to increase the number of classes and allow detection of multiple classes. Additionally, it explores a loss function suitable for imbalanced datasets.