Why the study?
Motion artifacts and baseline wander in ambulatory ECG measurements hinder ST-segment detection and remain challenging to completely suppress.
A novel attention-based Bi-LSTM deep learning model effectively suppresses motion artifacts and baseline wander in ambulatory ECGs, outperforming existing methods.
May aid artifact suppression in ambulatory ECG; leaves open prospective clinical validation.
An ambulatory electrocardiogram (ECG) is a comparatively recent technology and a gold standard for diagnosing heart activities. Motion artifacts and baseline wander in ambulatory ECG measurements may hinder the detection of vital ST segments due to varying electrical isolines. It is challenging to completely suppress the motion artifact and baseline wander from ambulatory ECG measurements. This work proposes a novel attention-based deep recurrent neural network using bidirectional long short-term memory (Bi-LSTM) and total variation denoising for motion artifact and baseline wander suppression. The attention mechanism, along with the Bi-LSTM, enhances the relevant features of the model by allocating a specific attention score, which improves the denoising performance. The proposed method is implemented on a Xilinx PYNQ-Z2 platform. The efficacy of the proposed technique is evaluated using the parameters, i.e., increment in signal-to-noise ratio, increment in correlation coefficient concerning ground truth signal, root mean square error, and cosine similarity index. The proposed denoising network is validated using MIT-BIH arrhythmia and MIT-BIH NSTDB database. Compared to state-of-the-art techniques, the proposed method seems superior in suppressing motion artifacts and baseline wander from corrupted ambulatory ECG measurements.
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Kumar et al. (2023) studied Ambulatory ECG measurements. Attention-based deep recurrent neural network using Bi-LSTM and total variation denoising vs. State-of-the-art techniques was evaluated on Increment in signal-to-noise ratio, increment in correlation coefficient, root mean square error, and cosine similarity index.
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