Engineering and Architecture Faculty, Department of Electrical & Electronics Engineering, Selcuk University, 42075 Konya, Turkey.
J Med Syst. 2009 Dec;33(6):435-45. doi: 10.1007/s10916-008-9205-1.
This paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented with integration of complex valued feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT). The aim of using CWT is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs. Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged) and 99.2% for the first and second classification tasks, respectively. All arrhythmias in training and test phases were classified correctly for both of the classification tasks.
本文提出了一种新的心电图（ECG）心律失常自动检测方法。该检测系统集成了复值特征提取和分类部分。在提出的方法的特征提取阶段，使用复离散小波变换（CWT）提取每个心律失常的特征值。使用 CWT 的目的是在不降低准确率的情况下压缩数据并减少网络的训练时间。获得的复值特征被用作复值人工神经网络（CVANN）的输入，用于 ECG 心律失常的分类。本研究从 MIT-BIH ECG 心律失常数据库中选择了十种 ECG 心律失常。该方法执行了两种不同的分类任务。在第一个分类任务（CT-1）中，逐一检查 CWT-CVANN 是否可以区分 ECG 心律失常和正常窦性节律。为此，改进了九个分类器并在 CT-1 中执行。第二个分类任务（CT-2）是用一个具有十个输出的复值分类器识别十种不同的 ECG 心律失常。通过以某种顺序混合心律失常来形成训练集和测试集。对于第一个和第二个分类任务，分别获得了 99.8%（平均）和 99.2%的准确率。对于两个分类任务，训练和测试阶段的所有心律失常都被正确分类。
