Dynamic-Deep: Tune ECG Task Performance and Optimize Compression in IoT Architectures

TL;DR

Dynamic-Deep is a self-adapting, task-aware ECG compression method for IoT devices that optimizes cloud cost savings while maintaining desired downstream task performance, demonstrated on ECG datasets with significant improvements.

Contribution

It introduces a novel self-adapting compression approach that balances ECG signal quality and task performance without cloud feedback, optimizing IoT-Cloud architectures.

Findings

Improves heart rate classification F1-score by up to 3 points.

Reduces cloud costs by 97% compared to no compression.

Achieves up to 83% higher compression gain than previous methods.

Abstract

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 prior to sending it to the Cloud to reduce the 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 self-adapting lossy compression solution that enables configuring a desired performance level on the downstream tasks while maintaining an optimized CG that reduces Cloud costs. We propose Dynamic-Deep, a task-aware compression geared for IoT-Cloud architectures. Our compressor is trained to optimize the CG while maintaining the performance requirement of the downstream tasks chosen out of a wide range. In deployment, the IoT edge device adapts the compression and sends an optimized representation for each data segment, accounting for the downstream task's desired performance without relying on feedback from the Cloud. 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 can be configured to improve HR classification F1-score in a wide range of requirements. One of which is tuned to improve the F1-score by 3 and increases CG by up to 83% compared to the previous state-of-the-art (autoencoder-based) compressor. Analyzing Dynamic-Deep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution.