| Application of Holter ECG Signal Analysis Based on Wavelet and Data Mining Technique |
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| 作者姓名： | 余辉 谢远国 周仲兴 吕扬生 |
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| 作者单位： | SchoolofPrecisionInstrumentsandOpto-ElectronicsEngineering,TianjinUniversity,Tianjin300072,China |
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| 摘 要： | A new model based on dyadic differential wavelet was developed for detecting the R peak in Holter ECG signal according to the design of data mining. The Mallat recursive filter algorithm was introduced to calculate wavelet and optimize the detection algorithm which is based on the equivalent filter technique. The detection algorithm has been verified by MIT arrhythmia database with a high efficiency of 99％. After optimization, the algorithm was put into clinical experiment and tested in the Air Force Hospital in Tianjin for about two months. After about 108 hearts beating test of more than 100 patients, the total efficient detection rate has reached 97％，Now this algorithm module has been applied in business software and shows perfect performance under the complex conditions such as the inversion of heart beating, the falling off of the electrodes, the excursion of base line and so on.
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| 关 键 词： | 二重微波 数字最小化 信号检测 心电图 R峰值探测 |
Application of Holter ECG Signal Analysis Based on Wavelet and Data Mining Technique |
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| Abstract: | A new model based on dyadic differential wavelet was developed for detecting the R peak in Holter ECG signal according to the design of data mining. The Mallat recursive filter algorithm was introduced to calculate wavelet and optimize the detection algorithm which is based on the equivalent filter technique. The detection algorithm has been verified by MIT arrhythmia database with a high efficiency of 99%. After optimization, the algorithm was put into clinical experiment and tested in the Air Force Hospital in Tianjin for about two months. After about 108 hearts beating test of more than 100 patients, the total efficient detection rate has reached 97%. Now this algorithm module has been applied in business software and shows perfect performance under the complex conditions such as the inversion of heart beating, the falling off of the electrodes, the excursion of base line and so on. |
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| Keywords: | wavelet data mining signal detection electrocardiogram dyadic wavelet R peak detection |
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