An Intelligent Hybrid Method for ECG Signal Compression Using Wavelet Transform and IWO Algorithm in Wireless Body Sensor Networks

Document Type : Original Article

Authors
Amir AliAbadian
Lotf Ali Yazdani
Department of Electrical and Biomedical Engineering, Shomal University
10.22034/jcse.2026.579425.1079
Abstract
Abstract: Wireless Body Sensor Networks (WBSNs) are increasingly essential in healthcare, enhancing patient comfort and quality of life. These systems support patients, doctors, and medical teams through services such as continuous medical monitoring, medication and health information delivery, memory assistance, home device control, and emergency communication. This paper proposes a novel hybrid method for ECG signal compression, combining the Discrete Wavelet Transform (DWT) with the Invasive Weed Optimization (IWO) algorithm. DWT effectively decomposes the ECG signal into various frequency bands, capturing critical signal features, while IWO optimizes the selection of wavelet coefficients to achieve maximum compression with minimal data loss. The proposed approach was tested on standard ECG datasets, showing improved compression ratios compared to conventional methods. The results confirm that this technique maintains high signal quality, making it highly effective for real-time ECG monitoring in WBSNs. This ensures efficient data transmission and reliable performance in modern healthcare applications.
Keywords
Body Sensor Networks
Medical Monitoring
ECG Signal Compression
Discrete Wavelet Transform
Invasive Weed Optimization
The CSI Journal on Computer Science and Engineering
Volume 20, Issue 2
August 2026
Pages 90-101