Abstract：Premature ventricular contraction (PVC) is a common heart rhythm disorders, which threatens humanity’s health, therefore accurate diagnosis of abnormal heart rhythms plays an important role to help humanity prevent cardiovascular disease. This paper proposed a diagnosis method based on ELM (extreme learning machine, ELM) to realize the discrimination of PVC from normal ECG (electrocardiograph) using the data from the MIT-BIH database as analysis object, and process of the method includes signal preprocessing, feature extraction and classification. The first step was to apply the wavelet transform combined with morphological filtering method for signal preprocessing to get the relatively clean signal, Then extracted feature parameters of QRS complex by using K-means clustering algorithm. Meanwhile, the calibration samples and prediction samples were established according to the feature parameters, and finally the ELM classifier for sample training match and classification recognition was adopted. 1260 cycles of signal were chosen to do experiment, and the results demonstrated that this algorithm could accurately diagnose the PVC, whose positive detection rate was up to 95% and sensitivity was up to 96% on average. Compared with other algorithms in the condition of similar detection accuracy, this algorithm can improve the real-time performance of the algorithm, which has high research value and certain practical value in mobile medical treatment and clinical medical treatment.
王瑞荣, 余小庆, 王 敏, 叶 杨. 基于极限学习机的室性早搏判别算法的实现[J]. 中国生物医学工程学报, 2017, 36(2): 158-164.
Wang Ruirong, Yu Xiaoqing, Wang Min, Ye Yang. Implementation of the Algorithm for Premature Ventricular Contraction Discrimination Based on Extreme Learning Machine. Chinese Journal of Biomedical Engineering, 2017, 36(2): 158-164.
[1] Benali R, Reguig FB, Slimane ZH. Automatic classification of heartbeats using wavelet neural network[J]. Journal of Medical Systems, 2012, 36(2): 883-892. [2] 杨波, 张跃. 基于多模板匹配的室性早搏判别算法[J]. 计算机工程, 2010, 36(16):291-293. [3] Shen Chiaping, Kao Wenchung, Yang Yuehying, et al. Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines[J]. Expert Systems with Applications, 2012, 39(9): 7845-7852. [4] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. [5] Hinton G. A practical guide to training restricted Boltzmann machines[J]. Momentum, 2010, 9(1): 926. [6] Huang Guangbin, Zhu Qinyu, Siew CK. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501. [7] 钟维, 黄启俊, 常胜, 等. 基于SOPC 的复合式生理信号检测系统设计[J]. 传感技术学报, 2014, 27(4): 446-451. [8] 张杰. Mallat算法分析及C语言实现[J]. 微计算机信息, 2010, 26(9):229-230. [9] Hasan MA, Ibrahimy MI, Reaz MBI. NN-Based R-peak detection in QRS complex of ECG signal[C]//4th Kuala Lumpur International Conference on Biomedical Engineering. Berlin: Springer-Verlag, 2008:217-220. s[10] 何云斌, 张晓瑞, 万静, 等.基于改进遗传模拟退火 K-means 的心电波形的分类研究[J]. 计算机应用研究, 2014, 31(11): 3328-3332. [11] Scardapane S, Comminiello D, Scarpiniti M, et al. Music classification using extreme learning machines [C] //2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). Trieste: IEEE, 2013: 377-381. [12] Rodriguez R, Mexicano A, Ponce-Medellin R, et al. Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals [C] //2014 International Symposium onComputer,Consumer and Control (IS3C). Taichung: IEEE, 2014: 1253-1258. [13] Tekeste T, Bayasi N, Saleh H, et al.Adaptive ECG interval extraction[C]//2015 IEEE International Symposium onCircuits and Systems (ISCAS). Lisbon: IEEE, 2015: 998-1001. [14] Mironovova M, BilaJ.Fastfourier transform for feature extraction and neural network for classification of electrocardiogram signals[C]//2015 Fourth International Conference on Future Generation Communication Technology (FGCT). Luton: IEEE, 2015: 1-6. [15] Rojo-Alvarez JL, Camps-Valls G, Caamano-Fernández AJ, et al. A review of Kernel methods in ECG signal classification [M]//ECG Signal Processing: Classification and Interpretation. London: Springer, 2012: 195-217. [16] Lagerholm M, Peterson C, Braccini G, et al. Clustering ECG complexes using Hermite functions and self-organizing maps[J]. IEEE Transactions on Biomedical Engineering, 2000, 47(7): 838-848. [17] Liu Nan, Cao Jiuwen, Koh Zhixiong, et al. Analysis of patient outcome using ECG and extreme learning machine ensemble [C] //2015 IEEE International Conference on Digital Signal Processing (DSP).Singapore city: IEEE, 2015: 1049-1052.
