Department of Electrical and Electronics Engineering, M.E.S. College of Engineering, Kerala, India.
J Med Syst. 2011 Dec;35(6):1617-30. doi: 10.1007/s10916-010-9439-6. Epub 2010 Mar 10.
Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) 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 of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.
基于数字处理心电图（ECG）信号可靠地检测心律失常对于为心脏患者提供适当和及时的治疗至关重要。由于 ECG 信号受到多种频率噪声的干扰，以及心脏节律中存在多种心律失常事件，因此计算机对异常 ECG 节律的解释是一项具有挑战性的任务。本文专注于模糊 C-均值（FCM）聚类概率神经网络（PNN）和多层前馈网络（MLFFN），用于区分八种类型的 ECG 节拍。从每个 ECG 节拍中提取第四阶自回归（AR）系数以及谱熵（SE）等参数，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。对麻省理工学院-贝斯以色列医院（MIT-BIH）心律失常数据库的广泛分析表明，FCM 聚类 PNN 在心脏心律失常分类方面优于 FCM 聚类 MLFFN，总体准确率分别为 99.05%和 97.14%。
