Author:
P.Balasubramani, S.Swathi KrishnaPublished in
Journal of Science Technology and Research( Volume , Issue )
ABSTRACT:
Wearable sensor nodes continuously monitor biomedical signals and generate large volumes of data. Since data transmission consumes about 75% of a sensor node’s power, reducing power usage remains critical for longer battery life. To address this, we propose a VLSI-based multi-level ECG compression method that significantly lowers both storage and power requirements. Our approach introduces a hybrid lossless compression algorithm combining Golomb–Rice coding with a bitmask-based dictionary selection method to enhance the bit compression rate. Golomb–Rice coding works efficiently for low-amplitude bit streams with repeated sequences of ones and zeros. Meanwhile, our bitmask-dictionary selection technique identifies and reuses frequent patterns, reducing memory usage for repeated random data. We implemented this scheme on the MIT-BIH arrhythmia database, achieving a higher compression ratio compared to existing architectures. This method ensures efficient ECG data handling for wearable devices while supporting real-time processing and conserving power through reduced transmission loads.
INTRODUCTION:
Wearable sensor nodes monitor physiological signals continuously and generate significant data volumes. These nodes use about 72% of their power for data transmission, making power conservation essential. In IoT-based wireless body area networks, sensors collect ECG signals and send the data to remote base stations for processing. Compressing data within the sensor node reduces transceiver use and saves power. Although lossy compression achieves high compression ratios (CR), it introduces reconstruction errors and computational complexity due to domain transformations. This increases storage needs and latency. Instead, we propose a VLSI-based multi-level ECG compression scheme using a lossless approach. Our method employs an adaptive linear predictor and encodes prediction differences with Golomb–Rice coding. We integrate a power-gating strategy and focus on minimizing area overhead and power consumption. Although current methods demand high computational resources, our proposed VLSI-based lossless compression offers real-time capability, low power usage, and optimal performance for wearable ECG monitoring systems.
