Mastoi Qurat-Ul-Ain, Wah Teh Ying, Mohammed Mazin Abed, Iqbal Uzair, Kadry Seifedine, Majumdar Arnab, Thinnukool Orawit
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq.
Life (Basel). 2022 Jun 6;12(6):842. doi: 10.3390/life12060842.
An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People's Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.
心电图（ECG）由五种不同的波形或特征（P、QRS和T）组成，这些波形或特征代表心脏内的电活动。识别各波的时间间隔和形态外观是从心电图信号中检测心脏异常的主要测量手段。本研究的重点是对五种不同类型的心跳进行分类，包括室性早搏（PVC）、左束支传导阻滞（LBBB）、右束支传导阻滞（RBBB）、起搏器心律（PACE）和房性早搏（APC），以确定心脏的确切状况。在分类之前，进行了广泛的特征提取实验，以从心电图信号中识别特定事件，如P波、QRS复合波和T波。本研究提出了融合技术，即双事件相关移动平均（DERMA）与分数傅里叶变换算法（FrlFT），以识别心电图信号的异常和正常形态事件。DERMA融合技术的目的是分析心电图峰值中的某些感兴趣区域，以确定所需位置，而FrlFT使用时频平面分析心电图波形。此外，利用检测到的心电图信号的最高和最低分量，如峰值、峰值之间的时间间隔和其他必要参数，建立了一个自动模型。在实验的最后阶段，训练了两种监督学习模型，即支持向量机和K近邻，以根据心电图信号对心脏状况进行分类。此外，本实验使用了两种类型的数据集，特别是有48名受试者的麻省理工学院-比哈尔心律失常数据库（MIT-BIH Arrhythmia）和新公布的绍兴和宁波人民医院（SPNH）数据库，该数据库包含超过10000名患者。实验装置的性能产生了压倒性的结果，显示出约99.99%的准确率、99.96%的灵敏度和99.9%的特异性。
