A nonparametric model for clustering ECG data

• Main Authors: YEH, TUNG-MING, 葉琮銘
• Other Authors: Tsair-chuan Lin
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/14383107724557841385

碩士 === 國立臺北大學 === 統計學系 === 96 === Time series data generally exists in daily life. When time series data have the periodic that goes round and begins again along with time, can subdivide into it again periodic for time sequence. Give examples to say, human circadian rhythms gene and body temperature and electrocardiogram. In the near future at clustering periodic time series data study up have more and more extensive trend. For overcoming parameter model the restriction of usage is on the linearly and stationary and symmetry data. The paper decides to set out from the angle of the nonparametric function periodic model. This text mainly aims at the data of the electrocardiogram periodic time series to carry on research and analysis. In regard to electrocardiogram data, it by many single electrocardiograms of period constitute, and single period electrocardiogram can see to make a few particular wave forms to constitute again, and each wave forms all have its characteristic in shoulding ascend, these characteristic again is be used as arrhythmias judgment up of basis. Therefore this paper attempt to build up the model of the electrocardiogram each waveform total, and hope to express a characteristic of form through the result of model, Then make use of these characteristics to carry on the disease of each single period electrocardiogram for clustering. Process in, because the wave of the electrocardiogram form mutually adds an effect and wave form structure not symmetry. Therefore make use of nonparametric estimate in of the general additive model and the nonparametric function period model. We can indicate single circulation period electrocardiogram data model with ECG addive model. According to the model, we can effectively extract a characteristic of waveform, and can single circulation period descend each characteristic of electrocardiogram waveform of mean, amplitude and phase the latent message estimate come out. Through two kinds of parameter-estimate-based clustering the method is the parameter differences test clustering method and parameter distance clustering method, give different object electrocardiogram series data clustering. Applied in 201 electrocardiogram record data from MIT-BIH arrhythmias database, 25 single period electrocardiogram data be divided into 5 clusters, and similarity can reach to 0.757215. Show put forth of the clustering method has certain centses ability.