Multistage Compression Technique for Electrocardiogram Signals with R-Peak Detection for Internet of Things Healthcare

Telemedicine and remote patient monitoring (RPM) are rapidly evolving fields transforming how healthcare is delivered. Remote monitoring solutions enable more efficient and effective care by connecting patients with healthcare providers in real-time with Internet-of-Things (IoT) enabled devices. However, the large scale of data transmission creates congestion in network traffic. This needs to be taken care of by applying a suitable data compression scheme for more effective and timely communication. In this work, the authors considered a two-level compression method for secure and efficient data transmission. Initially, the Wavelet Transform method utilizing the Daubechies mother wavelet is applied for noise suppression, data compression, and efficient encoding. Additionally, a novel algorithm is developed to convert 1-dimensional data to 2-dimensional data for enhanced analysis. Subsequently, the Singular Value Decomposition technique is employed to further reduce data size while maintaining compression efficiency. The proposed methodology demonstrates excellent performance for IoT-based communication, achieving a compression ratio of 98.2, percentage root mean square difference of 1.2034, quality score of 82.345, and signal-to-noise of 4.1858, and peak signal-to-noise ratio of 1.8013. These results highlight the effectiveness for telemedicine applications.