Towards Task-aware Signal Compression for Efficient Continuous Health Monitoring

High-precision multi-channel bio-signals are the basis of reliable and accurate wearable and implantable continuous health monitoring systems. However, the limitations of transmission bandwidth and computation resources of these systems pose heavy constraints on either the communication or direct processing of the large volume of physiological signals. Although signal compression can be adopted to compress the signals, most existing compression methods are computationally expensive and completely overlook the actual monitoring task purpose, which causes the discard of task-relevant information. Moreover, a complex reconstruction process is needed for further signal analysis at the cost of a heavy computational burden for downstream devices. We propose in this paper a novel flexible health monitoring framework where the signal is compressed with a low computation and hardware cost in-sensor compression matrix, trained in a task-aware fashion to preserve task-relevant information. The resulting compressed signals can be transmitted with significantly lower bandwidth, analyzed directly without a dedicated reconstruction process, or reconstructed with high fidelity. We demonstrate the effectiveness of our proposed framework by showcasing a seizure monitoring system. Prediction accuracy, sensitivity, false prediction rate, and signal reconstruction quality are reported under different compression ratios. Extensive experiments show that the proposed framework is accurate, with an average seizure prediction accuracy of 91.44%.