The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm...The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.展开更多
A framework of IIR two-channel bi-orthogonal filter banks is presented. The design of the analysis/synthesis systems reduces to the design of a single transfer function. The wavelet bases corresponding to the IIR m...A framework of IIR two-channel bi-orthogonal filter banks is presented. The design of the analysis/synthesis systems reduces to the design of a single transfer function. The wavelet bases corresponding to the IIR maximally flat filters are generated. Use the wavelet bases in wavelet packets analysis, take information entropy function as information cost function, adjust the best bases dynamically, and the section of signal's spectramfits its characteristics well. A lot of audio signals are processed. Compared to that of the wavelet transform and the wavelet packet transform using the Daubechies' wavelet which has the same order with our IIR wavelet, the properties of our system are better展开更多
文摘The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.
文摘A framework of IIR two-channel bi-orthogonal filter banks is presented. The design of the analysis/synthesis systems reduces to the design of a single transfer function. The wavelet bases corresponding to the IIR maximally flat filters are generated. Use the wavelet bases in wavelet packets analysis, take information entropy function as information cost function, adjust the best bases dynamically, and the section of signal's spectramfits its characteristics well. A lot of audio signals are processed. Compared to that of the wavelet transform and the wavelet packet transform using the Daubechies' wavelet which has the same order with our IIR wavelet, the properties of our system are better
基金湖南省自然科学基金(the Natural Science Foundation of Hunan Province of China under Grant No.05JJ30123)湖南省教育厅科学研究项目(the Scientific Research Program of Department of Education of Hunan Province China under Grant No.05C246)
