Ham F M, Han S
Florida Institute of Technology, Electrical Engineering, Melbourne 32901-6988, USA.
IEEE Trans Biomed Eng. 1996 Apr;43(4):425-30. doi: 10.1109/10.486263.
We have investigated the QRS complex, extracted from electrocardiogram (ECG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each ORS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity.
我们利用模糊自适应共振理论映射（ARTMAP）对从心电图（ECG）数据中提取的QRS复合波进行了研究，以对心律失常进行分类。分析了两种不同情况：正常和异常室性早搏（PVC）。基于麻省理工学院/贝斯以色列女执事医疗中心（MIT/BIH）数据库注释，从该数据库中提取正常和异常QRS复合波的心跳，进行缩放，并在带通滤波后加汉明窗，以得到每个QRS段100个样本的序列。从这些序列中的每一个，使用伯格最大熵方法生成两个线性预测编码（LPC）系数。这两个LPC系数以及QRS复合波段的均方值被用作每种情况的特征，以训练和测试一个模糊ARTMAP神经网络，用于对正常和异常PVC情况进行分类。测试结果表明，模糊ARTMAP神经网络能够以大于99%的特异性和97%的敏感性对心律失常进行分类。
