A computer-aided-diagnosis system for neuromuscular diseases using Mel frequency Cepstral coefficients

Belkhou, Abdelali; Jbari, Atman; Badlaoui, Othmane El

doi:10.1016/j.sciaf.2021.e00904

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Mel spectrogram features are extracted from the optical-acoustic signals collected by the Sagnac fiber optic acoustic sensing system [17]. The Mel spectrogram converts sound frequencies to Mel scale frequencies through triangular filter banks, and the logarithmic relationship between sound frequencies and Mel frequencies is described by Equation (2) [18], where Mel is the Mel frequency, f is the actual frequency in Hz.…”

Section: Feature Extraction Of Mel Spectrogramsmentioning

confidence: 99%

Research on lightweight synchronization recognition algorithms for multiple acoustic sources in a photoacoustic sensor system based on Sagnac interference

Zheng,

Tang,

Liu

et al. 2023

Real-Time Photonic Measurements, Data Management, and Processing VII

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Section: Feature Extraction Of Mel Spectrogramsmentioning

confidence: 99%

Research on lightweight synchronization recognition algorithms for multiple acoustic sources in a photoacoustic sensor system based on Sagnac interference

Zheng,

Tang,

Liu

et al. 2023

Real-Time Photonic Measurements, Data Management, and Processing VII

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Ensemble Learning Approach for Predicting Outstanding Debts for Housing Loans

Rungjaroen,

Whasphuttisit,

Jitsakul

2023

2023 7th International Conference on Information Technology (InCIT)

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Classification of Myopathy and Normal Electromyogram (EMG) Data with a New Deep Learning Architecture

TUNCER,

DOĞRU BOLAT

2023

Balkan Journal of Electrical and Computer Engineering

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Electromyograms (EMG) are recorded movements of nerves and muscles that help diagnose muscles and nerve-related disorders. It is frequently used in the diagnosis of neuromuscular diseases such as myopathy, which causes many changes in EMG signal properties. The most useful auxiliary test in the diagnosis of myopathy is EMG. Therefore, it has become imperative to identify computer-assisted anomalies with full accuracy and to develop an efficient classifier. In this study, a new machine learning method with a deep learning architecture that can score normal and myopathy EMG from the EMGLAB database is proposed. Using the discrete wavelet transform Coiflets 5 (Coif 5) wavelet, the EMG signals are decomposed into subbands and various statistical features are obtained from the wavelet coefficients. The success rates of the decision tree C4.5 algorithm, which is one of the traditional learning architectures, and the Long Short-term Memory (LSTM) algorithm, which is one of the deep learning architectures, were compared. Unlike the studies in the literature, with the LSTM algorithm, a 100% success rate was achieved with the proposed model. In addition, a real-time approach is presented by analyzing the test data classification time of the model.

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A computer-aided-diagnosis system for neuromuscular diseases using Mel frequency Cepstral coefficients

Cited by 3 publications

References 17 publications

Research on lightweight synchronization recognition algorithms for multiple acoustic sources in a photoacoustic sensor system based on Sagnac interference

Research on lightweight synchronization recognition algorithms for multiple acoustic sources in a photoacoustic sensor system based on Sagnac interference

Ensemble Learning Approach for Predicting Outstanding Debts for Housing Loans

Classification of Myopathy and Normal Electromyogram (EMG) Data with a New Deep Learning Architecture

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