Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
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DOI：
10.1155/2007/56215
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发表时间：
2007
影响因子：
1.9
通讯作者：
R. Andreão;J. Boudy
中科院分区：
文献类型：
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作者：
R. Andreão;J. Boudy
这项工作旨在为使用小波的心电图 (ECG) 分割问题提供新的见解。小波变换最初与隐马尔可夫模型（HMM）框架相结合，以进行节拍分割和分类。使用相同的框架实现并比较了心电图分析中常用的一组五个连续小波函数。所有实验都是在 QT 数据库上实现的，该数据库由多个个体的代表性数量的动态记录组成，并提供由医生制作的手动标签。我们的主要贡献依赖于所进行的一系列一致的实验。此外，在心跳分割和室性早搏（PVC）检测方面获得的结果与文献中报道的其他工作相当，与小波的类型无关。最后，通过在分割阶段组合两个小波函数的原始概念，我们实现了最佳性能。
This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.