Dynamic-Deep: ECG Task-Aware Compression

Monitoring medical data, e.g., Electrocardiogram (ECG) signals, is a common application of Internet of Things (IoT) devices. Compression methods are often applied on the massive amounts of sensor data generated before sending it to the Cloud to reduce storage and delivery costs. A lossy compression provides high compression gain (CG) but may reduce the performance of an ECG application (downstream task) due to information loss. Previous works on ECG monitoring focus either on optimizing the signal reconstruction or the task's performance. Instead, we advocate a lossy compression solution that allows configuring a desired performance level on the downstream tasks while maintaining an optimized CG. We propose Dynamic-Deep, a task-aware compression that uses convolutional autoencoders. The compression level is dynamically selected to yield an optimized compression without violating tasks' performance requirements. We conduct an extensive evaluation of our approach on common ECG datasets using two popular ECG applications, which includes heart rate (HR) arrhythmia classification. We demonstrate that Dynamic-Deep improves HR classification F1-score by a factor of 3 and increases CG by up to 83% compared to the previous state-of-the-art (autoencoder-based) compressor. Additionally, Dynamic-Deep has a 67% lower memory footprint. Analyzing Dynamic-Deep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution. To the best of our knowledge, Dynamic-Deep is the first proposal to focus on balancing the need for high performance of cloud-based downstream tasks and the desire to achieve optimized compression in IoT ECG monitoring settings.