Predicting acute hypotensive episode by using hybrid features and a neuro-fuzzy network

Abbasinia, Marzieh; Farokhi, Fardad; Javadi, Shahram

doi:10.3906/elk-1403-117

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…HFD proved to be sensitive to changes in the nervous system due to aging and AD [31]. Although the proposed method almost the same with study in [26], there some differences in this paper. In the study of [34], 127 HFD values were needed as features for EEG signal.…”

Section: Hfd Measurement For Lung Sound Classificationmentioning

confidence: 70%

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Lung sound classification using multiresolution Higuchi fractal dimension measurement

Rizal

Hidayat

Nugroho

et al. 2023

IJECE

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Lung sound is one indicator of abnormalities in the lungs and respiratory tract. Research for automatic lung sound classification has become one of the interests for researchers because lung disease is one of the diseases with the most sufferers in the world. The use of lung sounds as a source of information because of the ease in data acquisition and auscultation is a standard method in examining pulmonary function. This study simulated the potential use of Higuchi fractal dimension (HFD) as a feature extraction method for lung sound classification. HFD calculations were run on a series of k values to generate some HFD values as features. According to the simulation results, the proposed method could produce an accuracy of up to 97.98% for five classes of lung sound data. The results also suggested that the shift in HFD values over the selection of a time interval k can be used for lung sound classification.

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Section: Hfd Measurement For Lung Sound Classificationmentioning

confidence: 70%

“…In this study, MLP was applied with the altered number of hidden neurons, i.e., 15, 30, and 45 [26]. Meanwhile, for validation, we used N-fold cross validation (NFCV) [27].…”

Section: Hfd Measurement For Lung Sound Classificationmentioning

confidence: 99%