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Ultra-High Quality ECG Compression for IoMT Application Using Temporal Convolutional Auto-Encoder With Improved RVQ
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DOI：10.1109/jbhi.2026.3665032.png)
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Abstract
En 中文
Prolonged Electrocardiogram (ECG) monitoring through the Internet of Medical Things (IoMT) is vital for cardiac diagnosis yet generates prohibitive data volumes, posing significant challenges to storage and transmission. However, conventional ECG compression paradigms have plateaued, failing to push compression ratios higher under stringent fidelity constraints. To address these limitations, we propose an end-to-end architecture that synergizes a multi-granularity temporal-convolutional auto-encoder with Residual Vector Quantization (RVQ). The design introduces three complementary components: (1) RVQ integrated in the encoder–decoder pipeline to boost compression ratios; (2) a codebook-projection layer that increases codebook utilization and reconstruction fidelity; (3) periodicity-aware modeling that captures intrinsic ECG dynamics and further suppresses distortion. Extensive experiments on the MIT-BIH Arrhythmia Database show that the proposed method attains a compression ratio of 88× and Quality Score (QS) of 42.7, while keeping the Percentage Root Mean Difference (PRD) at 2.36% and the Percentage Root mean Difference Normalized (PRDN) at 17.56%, clearly surpassing existing techniques. Generalization is further assessed via zero-shot evaluation on the PhysioNet-2017 dataset. Overall, this paper presents an effective end-to-end compression framework, and experimental results corroborate its efficacy.
Keywords:
Electrocardiogram(ECG) compression
Big Data in IoMT
temporal auto-encoder
RVQ
periodic activation
discrete representation learning
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