Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic diagnosis of cardiovascular disease and reducing the massive workload of reviewing continuous ECGs. Hence, how to design an appropriate algorithm for objectively evaluating the multi-lead ECG recordings is particularly important. Despite the deep learning methods performing well in many fields, as a data-driven method, it may not be entirely suitable for ECG analysis due to the difficulty in obtaining sufficient data and the low signal-to-noise ratio of ECG recordings. In this study, with the aim of providing an accurate and automatic ECG quality assessment scheme, we propose an innovative ECG quality assessment algorithm based on hand-crafted statistical features and deep-learned spectral features.

Methods In this paper, a novel approach, combining the deep-learned Stockwell transform (S-Transform) spectrogram features and hand-crafted statistical features, is proposed for ECG quality assessment. Firstly, a double-input convolutional neural network (CNN) is established. Then, the S-Transform with a novel online augmentation scheme is performed on the multi-lead raw ECG signal received from one input layer to obtain proper time-frequency representation. After that, the CNN with three convolutional layers is employed to extract robust deep-learned features automatically. Simultaneously, the hand-crafted statistical features, including lead-fall, baseline drift, and R peak features, are calculated and fed into another input layer for feature fusion training. Finally, the deep-learned and hand-crafted features are concatenated and further fused by a fully connected layer for quality classification. Furthermore, a log-odds analysis scheme combining with a gradient-based method can localize the abnormal zone in time, frequency, and spatial domains.

Results and Conclusion Our proposed method is evaluated on a publicly available database with 10-fold cross-validation. The experimental results demonstrate that the proposed assessment algorithm reached a mean accuracy of 93.09%, a mean F1-score of 0.8472, and a sensitivity of 0.9767. Moreover, comprehensive experiments indicate that the fusion of CNN features and statistical features has complementary advantages and ideal interpretability, achieving end-to-end multi-lead ECG assessment with satisfying performance.

背景与目的心电图（ECG）质量评估对于心血管疾病的自动诊断和减少审查连续心电图的大量工作量具有重要意义。因此，如何设计合适的算法来客观评价多导联心电图记录就显得尤为重要。尽管深度学习方法在许多领域表现良好，但作为一种数据驱动的方法，由于难以获得足够的数据和心电图记录的信噪比低，它可能并不完全适合心电图分析。在本研究中，为了提供准确、自动的心电图质量评估方案，我们提出了一种基于手工统计特征和深度学习光谱特征的创新心电图质量评估算法。

方法在本文中，提出了一种结合深度学习的斯托克韦尔变换（S-Transform）频谱图特征和手工统计特征的新方法，用于心电图质量评估。首先，建立双输入卷积神经网络（CNN）。然后，对从一个输入层接收的多导联原始 ECG 信号执行具有新颖在线增强方案的 S 变换，以获得适当的时频表示。之后，使用具有三个卷积层的 CNN 来自动提取鲁棒的深度学习特征。同时，手工制作的统计特征，包括领先下降、基线漂移和 R 峰值特征，被计算并输入另一个输入层进行特征融合训练。最后，深度学习和手工制作的特征被一个完全连接的层连接并进一步融合以进行质量分类。此外，结合基于梯度的方法的对数优势分析方案可以在时间、频率和空间域中定位异常区域。

结果和结论我们提出的方法在一个公开可用的数据库上进行了评估，并进行了 10 倍交叉验证。实验结果表明，所提出的评估算法的平均准确率为 93.09%，平均 F1-score 为 0.8472，灵敏度为 0.9767。此外，综合实验表明，CNN特征和统计特征的融合具有互补优势和理想的可解释性，实现了端到端的多导联心电评估，性能令人满意。