Optimal wavelets for electrogastrography

Cintra, Renato J.; Tchervensky, I V; Dimitrov, Vassil S.; Mintchev, M.R.

doi:10.1109/iembs.2004.1403159

Cited by 19 publications

(18 citation statements)

References 15 publications

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“…The performance of each base wavelet was evaluated by the classifier performance after the cross-validation process. The 4th-order Daubechies yielded the best results, in agreement with de Sobral Cintra et al (33). As mentioned before, once the wavelet transform coefficients were obtained, very little relevant information existed in the first lower band and the two higher bands, and thus these coefficients were Correlation analysis was applied to the discrete WT coefficients to eliminate coefficients describing the same behavior for the whole set of subjects.…”

Section: Feature Selectionsupporting

confidence: 63%

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

Chacón¹,

Curilem²,

Acuña³

et al. 2009

Braz J Med Biol Res

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Section: Feature Selectionsupporting

confidence: 63%

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

Chacón¹,

Curilem²,

Acuña³

et al. 2009

Braz J Med Biol Res

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“…In this study, signals were decomposed using Daubechies mother wavelet of order ('db3') because of its suitability for biomedical signals like PPG and EGG [5][6][7][8]. Eighth level approximation coefficients matrices, which approximately correspond to the frequency range (0-0.1953) Hz are taken for further analysis based on available literature [6] and by considering Nyquist criterion.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Yacin

Chakravarthy

Manivannan

2011

Med Biol Eng Comput

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Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

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“…Usually, the goal is to minimize the L 2 norm between the signal and an approximation derived from its wavelet decomposition (Tewfik et al, 1992;Gupta et al, 2005), for compression purposes, for instance. These methods have been applied to medical classification problems, in order to match waveform signals (Maitrot et al, 2005;de Sobral Cintra et al, 2004). However, in our case, we do not intend to match a signal of reference: we want to find the wavelet basis that maximizes the retrieval performance of our system.…”

Section: Wavelet Adaptationmentioning

confidence: 99%

Wavelet optimization for content-based image retrieval in medical databases

Quellec

Lamard

Cazuguel

et al. 2010

Medical Image Analysis

175

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We propose in this article a Content-Based Image Retrieval method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system.

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Optimal wavelets for electrogastrography

Cited by 19 publications

References 15 publications

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Wavelet optimization for content-based image retrieval in medical databases

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