An Adaptive ECG Data Compression Method for High-Frequency Monitoring

With the growing prevalence of smart medical devices, electrocardiogram (ECG) signals play a crucial role in remote monitoring and health management. However, prolonged high-frequency ECG data acquisition generates large volumes of data, posing significant challenges in terms of storage and transmission. To address this issue, this paper proposes an ECG data compression method based on hierarchical Discrete Cosine Transform (DCT) and Adaptive Discrete Wavelet Transform (ADWT). The method combines multi-level compression with adaptive wavelet threshold adjustment, effectively reducing redundancy while preserving key physiological features, leading to improved compression ratio and minimized data distortion. Experimental results demonstrate that the proposed method achieves high compression efficiency and low reconstruction error across multiple ECG datasets, making it particularly suitable for highfrequency, long-duration ECG monitoring applications with significant practical potential.