Department of Electrical and Electronics Engineering, M.E.S. College of Engineering, Kuttippuram, 679 573, Kerala, India.
J Med Syst. 2011 Apr;35(2):179-88. doi: 10.1007/s10916-009-9355-9. Epub 2009 Aug 11.
The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.
心电图（ECG）作为一种非侵入性技术，用于检测和诊断心脏问题，其作用怎么强调都不为过。本文提出了一种模糊 C-均值（FCM）聚类概率神经网络（PNN），用于区分 8 种类型的 ECG 心拍。将其性能与使用反向传播算法训练的 FCM 聚类多层前馈网络（MLFFN）进行了比较。从每个 ECG 心拍中提取重要参数，并使用 FCM 聚类进行特征减少。聚类中心形成神经网络分类器的输入。使用 MIT-BIH 心律失常数据库进行的广泛分析表明，FCM 聚类 MLFFN 的平均分类准确率为 97.54%，FCM 聚类 PNN 的平均分类准确率为 99.58%。模糊聚类还提高了分类速度。结果表明，FCM 聚类 PNN 具有在 ECG 异常的计算机辅助诊断中的能力。
