Aziz Saira, Ahmed Sajid, Alouini Mohamed-Slim
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Sci Rep. 2021 Sep 21;11(1):18738. doi: 10.1038/s41598-021-97118-5.
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm's performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People's Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.
心电图（ECG）信号代表人类心脏的电活动，由几种波形（P、QRS和T）组成。每个波形的持续时间和形状以及不同波峰之间的距离用于诊断心脏病。在这项工作中，为了更好地分析心电图信号，提出了一种利用双事件相关移动平均（TERMA）和分数傅里叶变换（FrFT）算法的新算法。TERMA算法指定特定的感兴趣区域来定位所需的波峰，而FrFT在时频平面中旋转心电图信号以显示各个波峰的位置。所提出算法的性能优于现有算法。此外，为了自动对心脏病进行分类，使用估计的波峰、不同波峰之间的持续时间以及其他心电图信号特征来训练机器学习模型。大多数现有研究使用麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据库（只有48名患者）。然而，在这项工作中，最近报道的绍兴市人民医院（SPH）数据库（包含10000多名患者）被用于训练所提出的机器学习模型，这对于分类来说更具现实意义。跨数据库训练和测试取得了良好结果，这是我们所提出的机器学习模型的独特之处。
