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@article{184309,
        author = {Harsha S and Radha B L},
        title = {VLSI-Based ECG Compression Framework for Wearable Sensor Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {878-884},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184309},
        abstract = {In wireless body area networks (WBANs), continuous monitoring of biomedical signals such as ECG leads to the generation of large data volumes, which significantly increases transmission energy consumption. To address the challenges of storage and power efficiency, this work introduces a lossless ECG compression approach. The method combines Run-Length Encoding (RLE) with Golomb–Rice coding, forming a hybrid algorithm that improves compression performance. The proposed scheme is confirmed on the MIT-BIH arrhythmia database, where it achieves a compression ratio of 3.0. Furthermore, a dedicated VLSI architecture of the algorithm is realized in  90 nm CMOS technology, showing a power consumption of 153.27 µW at an operating frequency of 120 MHz and a supply voltage of 1.2 V, while occupying only 28.47 mm² of chip area.},
        keywords = {Biomedical Signal Processing, Data Storage Reduction, Electrocardiogram (ECG), Golomb–Rice Coding, Lossless Compression, Low-Power VLSI, Run-Length Encoding (RLE), Wireless Body Area Networks (WBAN), 90 nm CMOS.},
        month = {September},
        }