Compressed Sensing Approaches to Physiological Signals for Remote Patient Monitoring
With the advancement in physiological signal acquisition and processing, the understanding of disease detection and diagnosis has improved. However, the increased data volume and power consumption call for effective data compression, transmission, and processing methods, particularly in telemonitoring healthcare applications. A new research area is exploring the integration of Compressed Sensing (CS) and Compressed Learning (CL) with physiological signals to manage large amounts of data, conserve power, and optimize transmission bandwidth. The research emphasises the use of CS and CL in managing physiological signals such as electrocardiography(ECG), electromyography (EMG), and electroencephalography (EEG) and implementing them in hardware, such as FPGA, to overcome these challenges.