Pashoutan Soheil, Baradaran Shokouhi Shahriar
MSc, Department of Electrical, Iran University of Science and Technology, Tehran, Iran.
PhD, Department of Electrical, Iran University of Science and Technology, Tehran, Iran.
J Biomed Phys Eng. 2021 Aug 1;11(4):535-550. doi: 10.31661/jbpe.v0i0.1112. eCollection 2021 Aug.
Cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ECG) signals. Moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain.
In this paper, an image-based and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of VF, VT, SVT and the normal signal.
In this simulation study, the ECG data used are collected from 3 databases, including Boston Beth University Arrhythmias Center, Creighton University, and MIT-BIH. The proposed algorithm was implemented using MATLAB R2015a software and its simulation. At first, the signal is transmitted to the state space using an optimal time delay. Then, the optimal delay values are obtained using the particle swarm optimization algorithm and normalized mutual information criterion. Furthermore, the result is considered as a binary image. Then, 19 features are extracted from the image and the results are presented in the multilayer perceptron neural network for the purpose of training and testing.
In order to classify N-VF, VT-SVT, N-SVT, VF-VT, VT-N-VF, N-SVT-VF, VT-VF-SVT and VT-VF-SVT-N in the conducted experiments, the accuracy rates were determined at 99.5%, 100%, 94.98%, 100%,100%, 100%, 99.5%, 96.5% and 95%, respectively.
In this paper, a new approach was developed to classify the abnormal signals obtained from an ECG such as VT, VF, and SVT compared to a normal signal. Compared to Other related studies, our proposed system significantly performed better.
心律失常被认为是最严重的健康状况之一；因此，对心电图（ECG）信号进行准确快速的诊断对于这些病症至关重要。此外，由于这些信号在时域中非常相似，心脏病专家仅凭肉眼很难进行诊断。
本文提出了一种基于图像和机器学习的方法，以研究室颤（VF）、室性心动过速（VT）、室上性心动过速（SVT）这三种心律失常与正常信号之间的差异。
在本模拟研究中，使用的心电图数据来自3个数据库，包括波士顿贝斯以色列女执事医疗中心心律失常中心、克里顿大学和麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库。所提出的算法使用MATLAB R2015a软件及其模拟实现。首先，使用最优时间延迟将信号传输到状态空间。然后，使用粒子群优化算法和归一化互信息准则获得最优延迟值。此外，将结果视为二值图像。然后，从图像中提取19个特征，并将结果呈现给多层感知器神经网络进行训练和测试。
在进行的实验中，为了对N - VF、VT - SVT、N - SVT、VF - VT、VT - N - VF、N - SVT - VF、VT - VF - SVT和VT - VF - SVT - N进行分类，准确率分别确定为99.5%、100%、94.98%、100%、100%、100%、99.5%、96.5%和95%。
本文开发了一种新方法，用于将心电图获得的异常信号（如VT、VF和SVT）与正常信号进行分类。与其他相关研究相比，我们提出的系统表现明显更好。
