Berrahou Nadia, El Alami Abdelmajid, Mesbah Abderrahim, El Alami Rachid, Berrahou Aissam
Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
ENSIAS, Mohammed V University, Morocco.
Comput Methods Biomech Biomed Engin. 2024 Jul 17:1-20. doi: 10.1080/10255842.2024.2378105.
The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.
利用心电图（ECG）信号对患者间心电图数据进行心律失常检测分类面临重大挑战。尽管深度学习方法近来激增，但患者间心电图分类的性能仍存在显著差距。在本研究中，我们通过采用一维卷积神经网络（CNN）来利用心动周期的形态和时间特征，引入了一种用于心律失常检测中ECG分类的创新方法。通过使用一维卷积神经网络层，我们自动捕捉ECG数据的形态属性，从而能够表示R波峰值周围ECG波形的形状。此外，我们纳入了四个RR间期特征以提供时间背景，并探索熵率作为ECG信号分类特征提取技术的潜在应用。因此，分类层受益于时间特征和学习到的特征的结合，从而实现最终的心律失常分类。我们使用麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据集验证我们的方法，采用患者内和患者间范式进行模型训练和测试。通过在INCART数据集上评估该模型来评估其泛化能力。在五分类的患者内分类中，该模型在2折和5折交叉验证下的平均准确率分别达到99.13%和99.17%。在三分类和五分类的患者间分类中，该模型的平均准确率分别达到98.73%和97.91%。对于INCART数据集，该模型在三分类时的平均准确率达到98.20%。实验结果表明，与现有最先进模型相比，所提出的模型在识别心律失常方面具有优越性。因此，所提出的模型具有更强的泛化能力，并且有潜力作为一种有效解决方案，用于识别实际应用中存在类别不平衡的真实世界数据集中的心律失常。
