Elgendi Mohamed, van der Bijl Kirina, Menon Carlo
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland.
Diagnostics (Basel). 2023 Nov 20;13(22):3479. doi: 10.3390/diagnostics13223479.
The rise in cardiovascular diseases necessitates accurate electrocardiogram (ECG) diagnostics, making high-quality ECG recordings essential. Our CNN-LSTM model, embedded in an open-access GUI and trained on balanced datasets collected in clinical settings, excels in automating ECG quality assessment. When tested across three datasets featuring varying ratios of acceptable to unacceptable ECG signals, it achieved an F1 score ranging from 95.87% to 98.40%. Training the model on real noise sources significantly enhances its applicability in real-life scenarios, compared to simulations. Integrated into a user-friendly toolbox, the model offers practical utility in clinical environments. Furthermore, our study underscores the importance of balanced class representation during training and testing phases. We observed a notable F1 score change from 98.09% to 95.87% when the class ratio shifted from 85:15 to 50:50 in the same testing dataset with equal representation. This finding is crucial for future ECG quality assessment research, highlighting the impact of class distribution on the reliability of model training outcomes.
心血管疾病的增加使得准确的心电图（ECG）诊断成为必要，高质量的ECG记录至关重要。我们的CNN-LSTM模型嵌入在一个开放获取的图形用户界面（GUI）中，并在临床环境中收集的平衡数据集上进行训练，在自动进行ECG质量评估方面表现出色。当在三个具有不同可接受与不可接受ECG信号比例的数据集上进行测试时，它的F1分数在95.87%至98.40%之间。与模拟相比，在真实噪声源上训练该模型显著提高了其在实际场景中的适用性。该模型集成到一个用户友好的工具箱中，在临床环境中具有实际应用价值。此外，我们的研究强调了在训练和测试阶段平衡类表示的重要性。当在具有相同表示的同一测试数据集中类比例从85:15变为50:50时，我们观察到F1分数从98.09%显著变化到95.87%。这一发现对未来的ECG质量评估研究至关重要，突出了类分布对模型训练结果可靠性的影响。
