Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia.
Department of Cardiology & Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang 30126, Indonesia.
Sensors (Basel). 2022 Mar 17;22(6):2329. doi: 10.3390/s22062329.
Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.
医生在常规临床实践中手动解释心电图 (ECG) 信号形态。这项活动是一项单调而抽象的任务，依赖于理解心电图波形含义的经验，包括 P 波、QRS 复合体和 T 波。这种手动过程取决于信号质量和导联数量。基于深度学习 (DL) 的 ECG 信号分类已经产生了自动解释；然而，所提出的方法仅用于特定的异常情况。当 ECG 信号形态变化为其他异常时，它无法自动进行。为了推广自动解释，我们旨在描绘 ECG 波形。然而，描绘过程的输出仅为 P 波、QRS 复合体和 T 波的 ECG 波形持续时间类别。它应该与医学知识规则相结合，以产生异常解释。所提出的模型应用于心房颤动 (AF) 的识别。本研究符合 RR 不规则且基本振荡中无 P 波的 AF 标准，以实现更准确的识别。Physionet 的 QT 数据库用于开发描绘模型，并通过 The Lobachevsky University Database 进行验证。结果表明，我们的描绘模型运行正常，具有 98.91%的灵敏度、99.01%的精度、99.79%的特异性、99.79%的准确性和 98.96%的 F1 分数。我们使用大约 4058 个正常窦性节律记录和 1804 个 AF 记录来识别来自三个数据集的 AF 条件。综合测试产生了更高的阴性预测值和阳性预测值。这意味着所提出的模型可以从 ECG 信号描绘中识别 AF 条件。我们的方法可以通过这些结果为 AF 诊断做出重大贡献。
