Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia.
Comput Methods Programs Biomed. 2014 Mar;113(3):919-26. doi: 10.1016/j.cmpb.2013.12.002. Epub 2014 Jan 8.
ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
心电图信号是诊断心房传导病变的重要信息源。然而，通过肉眼观察进行诊断是一项艰巨的任务。本工作提出了一种新颖的基于终端小波包变换（WPT）子信号平均帧能量（AFE）的房颤小波特征提取方法。采用概率神经网络（PNN）进行分类。结果表明，该方法在自动诊断过程中是一种潜在有效的鉴别器。所采用的 ECG 信号取自 MIT-BIH 数据库，与正常 ECG 一起用于分类不同的心律失常。同时，还对几种已发表的方法进行了比较。最佳的识别率选择是 AFE。该分类性能的准确率达到了 97.92%。还建议在加性白高斯噪声（AWGN）环境中分析所提出的系统；对于 0dB，为 55.14%，对于 5dB，为 92.53%。研究得出结论，采用自动化分类方法是值得进一步研究的，需要更大的样本进行验证和扩展。
