Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices
Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices
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DOI：
10.1007/s40846-020-00588-7
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发表时间：
2021-04-16
影响因子：
2
通讯作者：
Li, Jianqing
中科院分区：
文献类型：
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作者：
Fu, Fan;Xiang, Wentao;Li, Jianqing
目的可穿戴设备采集的心电信号容易受到环境噪声和伪影的干扰，其信噪比明显低于医院心电仪。为了满足通过可穿戴设备监测心脏病的要求，消除无用或质量差的ECG信号（例如，超前下降和低SNR）可以通过信号质量评估算法来解决。方法为弥补现有心电质量评估系统的不足，构建了一个由联想H3设备采集的心脏病患者可穿戴心电信号数据集。然后，本文比较了三种机器学习算法的性能，传统的支持向量机（SVM）、最小二乘支持向量机（LS-SVM）和长短期记忆（LSTM）算法。不同的非形态信号质量指数（即，从原始ECG信号中提取的近似熵（ApEn）、样本熵（SaEn）、模糊测度熵（FMEn）、赫斯特指数（HE）、峰度（K）和功率谱密度（PSD）特征作为输入到三种算法中。结果以真阳性率、真阴性率、灵敏度和准确度为指标评价各方法的性能，LSTM算法在这些指标上的结果最好（分别为97.14%、86.8%、97.46%和95.47%）。结论在3种算法中，基于LSTM的质量评估方法最适合联想H3设备采集的信号。结果还表明，统计特征的组合可以有效地评价心电信号的质量。
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.