Wavelet analysis of an electrogastrogram database

Barroso-Alvarado, Alain Javier; Tovar-Corona, Blanca; Contreras-Uribe, Tania J.; Santiago-Godoy, Rafae; Gonzalez-Navarro, Yesenia Eleonor; Solis-Villela, Jose Gonzalo; Hernandez-Ledesma, Carlos; Curiel-Obregon, Andrea Veronica; Jiménez, Laura Ivoone Garay

doi:10.1109/iceee.2011.6106644

Cited by 7 publications

(8 citation statements)

References 4 publications

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“…Finally, the results we obtained by evaluating our proposed line length method were superior to the EGG results of previous studies. 9,18,20,26 Although the results obtained by other researchers are promising, they did not compare the FD and GP patient groups to healthy controls. In the literature, some studies have compared FD patients with a healthy group of control subjects 11,19,25,27 and some researchers have compared GP patients with healthy subjects 9,26,31 or stomach cancer subjects.…”

Section: Resultsmentioning

confidence: 96%

“…Researchers found that the DWT method is useful for extracting EGGs correctly. 9,[19][20][21]26 Furthermore, the line length method reflects the waveform dimensionality variation and is sensitive to changes in the signal amplitude and frequency. Different line length features are calculated from each wavelet sub-signal, which are obtained by passing the EGG signal through a bank of wavelet filters.…”

Section: Discussionmentioning

confidence: 99%

“…8,18,31 In this technique, a gamma camera creates images of the subject's stomach from the front and the back side using emitted gamma radiation. 20,24 Although all GP patients had a history of chronic insulin-dependent diabetes mellitus, the cases in which radioscintigraphy test results did not show delayed gastric emptying were excluded from the study.…”

Section: Gp Groupmentioning

confidence: 99%

“…In contrast, no clear differences were found in other studies. 9,[18][19][20][21][22][23] Therefore, in this work, the line length method based on amplitude-and frequencydependent linear features was used to discriminate EGG signals. Furthermore, a comparison of the variations and frequency domain-related features for the three groups was performed for the pre-and postprandial conditions.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

Kafee

Akan

2018

Proc Inst Mech Eng H

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Electrogastrogram is used for the abdominal surface measurement of the gastric electrical activity of the human stomach. The electrogastrogram technique has significant value as a clinical tool because careful electrogastrogram signal recordings and analyses play a major role in determining the propagation and coordination of gastric myoelectric abnormalities. The aim of this article is to evaluate electrogastrogram features calculated by line length features based on the discrete wavelet transform method to differentiate healthy control subjects from patients with functional dyspepsia and diabetic gastroparesis. For this analysis, the discrete wavelet transform method was used to extract electrogastrogram signal characteristics. Next, line length features were calculated for each sub-signal, which reflect the waveform dimensionality variations and represent a measure of sensitivity to differences in signal amplitude and frequency. The analysis was carried out using a statistical analysis of variance test. The results obtained from the line length analysis of the electrogastrogram signal prove that there are significant differences among the functional dyspepsia, diabetic gastroparesis, and control groups. The electrogastrogram signals of the control subjects had a significantly higher line length than those of the functional dyspepsia and diabetic gastroparesis patients. In conclusion, this article provides new methods with increased accuracy obtained from electrogastrogram signal analysis. The electrogastrography is an effective and non-stationary method to differentiate diabetic gastroparesis and functional dyspepsia patients from the control group. The proposed method can be considered a key test and an essential computer-aided diagnostic tool for detecting gastric myoelectric abnormalities in diabetic gastroparesis and functional dyspepsia patients.

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Section: Resultsmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Gp Groupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

Kafee

Akan

2018

Proc Inst Mech Eng H

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“…Classical Fourier domain approaches, [6][7][8][9] like bandpass filters (BPFs) and autoregressive models, have an inherent limitation on their applicability to nonstationary EGG signals whose frequency, amplitude, and phase widely vary across measurement timings and subjects. 5 To circumvent this problem, researchers 10,11 have used wavelets to extract gastric signals amidst severe contaminations from biological artifacts and random noise. Nevertheless, the performance of wavelet denoising crucially depends on the proper selection of mother wavelet.…”

Section: Introductionmentioning

confidence: 99%

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

et al. 2018

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Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

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Toward Early Abnormalities Detection on Digestive System: Multi-Features Electrogastrogram (EGG) Signal Classification based on Machine Learning

Amri

Yuliani

Simbolon

et al. 2021

2021 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

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Wavelet analysis of an electrogastrogram database

Cited by 7 publications

References 4 publications

Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

Toward Early Abnormalities Detection on Digestive System: Multi-Features Electrogastrogram (EGG) Signal Classification based on Machine Learning

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