Integrated Signal Encoding and Analysis System for Actigraphy-based Long-term Monitoring of Periodic Limb Movements in Sleep.

There has been a boom in the development of wearable devices for wellness and healthcare applications. Numerous studies have been conducted on the utility of employing wearable devices for the long-term monitoring of biosignals. Despite their efficacy, the potential for practical implementation faces many hurdles such as memory usage, power consumption, denoising, and efficient data transmission. Of the many wearables being used, the actigraph has been a popular choice amongst experts for identifying motion abnormalities such as periodic leg movements (PLMs) in sleep and the activities of patients suffering from various medical illnesses. In this paper, we present an efficient pulse code modulation based, 3-bit, signal encoding technique, which when applied to long-term (6-8 hours), 16-bit sleep actigraphy signals, generates 3-bit encoded, accelerometry data with an average compression ratio of 92%, an average increase in the signal-to-noise (SNR) ratio by 20 dB and an average reduction of memory usage by 92%. The proposed technique also eliminates the need to apply filters for denoising, by retaining only characteristic signal information in the quantized version. The proposed technique, in general, could be applied to accelerometer-based wearables and has the potential to provide efficient memory and power usage in long-term monitoring applications.