College of Electrical and Mechanical Engineering, National University of Sciences & Technology, Pakistan.
Hamad Bin Khalifa University, Qatar.
Comput Biol Med. 2022 Jun;145:105425. doi: 10.1016/j.compbiomed.2022.105425. Epub 2022 Apr 2.
A suitable temporal and spectral processing of the electrocardiogram (ECG) signals can facilitate the visual interpretation and discrimination between known patterns for classification. This paper proposes a non-invasive hybrid neural network and time-frequency (TF) based method to detect and classify commonly found cardiac abnormalities in ECG signals including congestive heart failure, ventricular tachyarrhythmia, intracardiac atrial fibrillation, arrhythmia, malignant ventricular ectopy, normal sinus rhythm, and postictal heart rate oscillations in partial epilepsy. Non-stationary raw ECG signals are collected from an online healthcare dataset source 'PhysioBank' that contains physiologic signals. These temporal signals are processed through Wigner-Ville distribution to produce high-resolution and concentrated TF images depicting specific visual patterns of cardiac abnormalities. The TF images are used to extract the abnormality parameters with the help of medical experts with good diagnostic accuracy. Principal component analysis (PCA) is employed for feature reduction and important features selection from the ECG signals. The selected features are used for training the multilayer feed-forward artificial neural network (ANN) for detection and classification while training parameters like the number of epochs, activation functions, and the learning rate is suitably selected with appropriate stopping criteria. Experimental results demonstrate the effectiveness of the hybrid neural-TF approach using PCA for abnormality detection and classification.
对心电图（ECG）信号进行适当的时频处理，可以方便视觉解释和已知模式之间的分类区分。本文提出了一种基于非侵入式混合神经网络和时频（TF）的方法，用于检测和分类心电图信号中常见的心脏异常，包括充血性心力衰竭、室性心动过速、心内心房颤动、心律失常、恶性室性早搏、窦性正常节律和部分癫痫发作后的心率波动。非平稳原始 ECG 信号从包含生理信号的在线医疗保健数据集来源 'PhysioBank' 中采集。这些时间信号通过维格纳-维尔分布进行处理，生成高分辨率和集中的 TF 图像，描绘心脏异常的特定视觉模式。TF 图像用于提取异常参数，同时借助具有良好诊断准确性的医学专家进行帮助。主成分分析（PCA）用于从 ECG 信号中进行特征降维和重要特征选择。选择的特征用于训练多层前馈人工神经网络（ANN）进行检测和分类，同时适当地选择训练参数，如 epoch 数、激活函数和学习率，并使用适当的停止准则。实验结果证明了基于 PCA 的混合神经-TF 方法在异常检测和分类方面的有效性。
