Key result
CNN-LSTM-Attention model improves ECG reconstruction over standard networks, maintaining SNR above ~20 dB across compression ratios.
Why the study?
Does a CNN-LSTM-Attention hybrid deep learning model improve ECG signal compression and reconstruction accuracy compared to single CNN, CNN-LSTM, and traditional compressed sensing algorithms on the MIT-BIH Arrhythmia dataset?
Does a CNN-LSTM-Attention hybrid deep learning model improve ECG signal compression and reconstruction accuracy compared to single CNN, CNN-LSTM, and traditional compressed sensing algorithms on the MIT-BIH Arrhythmia dataset?
Integrating a multi-head self-attention mechanism into a CNN-LSTM architecture improves the accuracy and stability of ECG signal reconstruction from compressed data compared to standard CNN, CNN-LSTM, and traditional compressed sensing algorithms.
Hybrid CNN-LSTM-Attention model improves ECG compression fidelity; leaves open real-world validation for remote monitoring.
The high prevalence of cardiovascular diseases and the extensive application wearable electrocardiogram (ECG) devices for long-term monitoring have posed significant challenges for the transmission, storage, and real-time processing of massive amounts of ECG data. Consequently, efficient ECG compression and reconstruction have become a research priority in remote ECG monitoring. Traditional compressed sensing is complex and has high computational overhead, while single deep learning models cannot simultaneously extract local waveforms and model temporal dependencies. To address these shortcomings in the reconstruction process, this paper presents a CNN-LSTM-Attention hybrid model. This model utilizes a convolutional neural network (CNN) to capture local ECG waveform features, employs a long short-term memory (LSTM) network to learn long-term temporal dependencies, and introduces an attention mechanism to weight and fuse key diagnostic features, enabling accurate focus on key components including the QRS complex and ST segment. Experimental results on the MIT-BIH Arrhythmia dataset demonstrate that across the full compression range of 0.1–0.9, the proposed model achieves favorable comprehensive performance. Its PRD is stabilized at 10–12%, the SNR stays above 20 dB, and the RMSE is mostly lower than 0.25 mV. In terms of reconstruction accuracy and stability, our model outperforms the single CNN and CNN-LSTM models by a large margin.
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Liu et al. (2026) studied Normal sinus rhythms and 14 common arrhythmia categories (MIT-BIH Arrhythmia Dataset) (n=48). CNN-LSTM-Attention hybrid model with Discrete Cosine Transform (DCT) compression vs. Single CNN model, CNN-LSTM model, Orthogonal Matching Pursuit (OMP), and Basis Pursuit (BP) was evaluated on ECG signal reconstruction performance measured by Percentage Root Mean Square Error (PRD), Signal-to-Noise Ratio (SNR), and Root Mean Square Error (RMSE) across compression ratios of 0.1 to 0.9. The CNN-LSTM-Attention model achieved a PRD of 10–12%, SNR above 20 dB, and RMSE mostly below 0.25 mV across compression ratios of 0.1–0.9, outperforming single CNN and CNN-LSTM models.
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