Analysis of K-RLE Data Compression Performance on ECG Signals for an Internet of Things Application

A wireless biomedical monitoring system begins with a wireless sensor node, the front-end device for acquiring data from various sensors. Data is sent to the cloud before being displayed to users via the dashboard. The front-end device presents significant challenges, particularly for clinical applications. In addition to collecting accurate bio-signal data, it must meet certain critical requirements such as portability, real-time monitoring and power efficiency. The data transmission process consumes significant energy in the sensor node. As a result, reducing the number of data transmissions becomes critical to reduce power consumption. This paper focuses on the sensor node design tailored explicitly for biomedical applications and introduces an ECG data compression implementation. This study examines both the sensor node's performance and the compression algorithm. On the sensor node, a lossy compression technique known as K-RLE is used to reduce the number of transmissions. The reconstructed ECG data shows that the sensor node effectively preserves all significant ECG signal events up to K = 15.