Adaptive wavelets for signal classification and compression

Kadambe, S.; Srinivasan, Pramila

doi:10.1016/j.aeue.2005.01.006

Cited by 33 publications

(21 citation statements)

References 22 publications

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“…Zhang, 1993). In (Billings & Wei, 2005;Kadambe & Srinivasan, 2006;Mellit, Benghamen, & Kalogirou, 2006;Xu & Ho, 1999) an adaptive WN was used. In (Chen, et al, 2006) a local linear WN was proposed.…”

Section: Structure Of a Wavelet Networkmentioning

confidence: 99%

“…in short term load forecasting, (Bashir & El-Hawary, 2000;Benaouda, Murtagh, Starck, & Renaud, 2006;Gao & Tsoukalas, 2001;Ulugammai, Venkatesh, Kannan, & Padhy, 2007;S. J. Yao, Song, Zhang, & Cheng, 2000), in time series prediction, (Cao, et al, 1995;Chen, Yang, & Dong, 2006;Cristea, Tuduce, & Cristea, 2000), signal classification and compression, (Kadambe & Srinivasan, 2006;Pittner, Kamarthi, & Gao, 1998;Subasi, Alkan, Koklukaya, & Kiymik, 2005), signal denoising, (Z. Zhang, 2007), static, dynamic (Allingham, West, & Mees, 1998;Oussar & Dreyfus, 2000;Oussar, Rivals, Presonnaz, & Dreyfus, 1998;Pati & Krishnaprasad, 1993;Postalcioglu & Becerikli, 2007; Q.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet neural networks: A practical guide

2013

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Abstract-Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.

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Section: Structure Of a Wavelet Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet neural networks: A practical guide

2013

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“…Any disturbance in the regular rhythmic activity of the heart (e.g., amplitude, duration, the shape of rhythm) is termed arrhythmia. Various approaches have been taken in the detection and classification of ECG beats (Friesen et al, 1990;Kadambe and Srinivasan, 2006;Meyer et al, 2006;Osowski and Nghia, 2002). The basic method is the Pan-Tompkins algorithm (Hamilton and Tompkins, 1986;Pan and Tompkins, 1985).…”

Section: Introductionmentioning

confidence: 99%

An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

Jiang¹,

Yang²,

Yang³

2013

Industrial Engineering and Management Systems

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Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

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“…Wavelet networks were used with great success in short term load forecasting, [4], in time series prediction, [5], signal classification and compression, [6], static, dynamic [1] and nonlinear modeling [7], nonlinear static function approximation, [8]. Fi-nally, [9] proposed WN as a multivariate calibration method for simultaneous determination of test samples of copper, iron, and aluminum.…”

Section: Introductionmentioning

confidence: 99%

Model Identification in Wavelet Neural Networks Framework

Zapranis

Alexandridis

2009

IFIP Advances in Information and Communication Technology

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The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as well as sampling techniques proposed in previous works were compared to derive an algorithm for selecting the correct topology of a WN. Finally, in variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented.

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Adaptive wavelets for signal classification and compression

Cited by 33 publications

References 22 publications

Wavelet neural networks: A practical guide

Wavelet neural networks: A practical guide

An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

Model Identification in Wavelet Neural Networks Framework

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