Key result
LSTM-based assessment evaluates wearable ECG signal quality with ~95% accuracy.
Why the study?
Wearable ECG signals have significantly lower signal-to-noise ratios than hospital ECG machines, requiring effective quality assessment algorithms to eliminate poor-quality signals.
Does the LSTM algorithm improve the quality assessment of wearable ECG signals compared to SVM and LS-SVM?
Does the LSTM algorithm improve the quality assessment of wearable ECG signals compared to SVM and LS-SVM?
An LSTM-based machine learning approach using statistical features provides highly accurate quality assessment of wearable ECG signals, outperforming traditional SVM methods.
Answers are grounded in this paper and cite their sources.
May support LSTM for wearable ECG quality control; leaves open clinical validation before routine adoption.
Purpose Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms. Methods To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input. Results The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively). Conclusions Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.
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Fu et al. (2021) studied Heart disease (premature beats, atrial fibrillation, arrhythmia) (n=200). Long short-term memory (LSTM) algorithm vs. Support vector machine (SVM) and least-squares SVM (LS-SVM) algorithms was evaluated on Accuracy of ECG signal quality assessment. The LSTM-based quality assessment method achieved the highest accuracy of 95.47% for evaluating wearable ECG signals collected by Lenovo H3 devices.
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