Classification of sEMG Signals for Diagnosis of Unilateral Posterior Crossbite in Primary Dentition using Fast Fourier Transform and Logistic Regression

Kalani, H.; Abbasi, E.

doi:10.22044/jadm.2022.10908.2234

Document Type : Original/Review Paper

Authors

H. Kalani ¹
E. Abbasi ²

¹ Department of Mechanical Engineering, Sadjad University, Mashhad, Iran.

² Medical Faculty-Islamic Azad University of Mashhad, Mashhad, Iran.

https://doi.org/10.22044/jadm.2022.10908.2234

Abstract

Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite (UPCB) in the primary dentition from the surface electromyography (sEMG) activity of masticatory muscles. In this study, 40 children (4–6y) were selected and divided into UPCB (n = 20) and normal occlusion (NOccl; n = 20) groups. The preferred chewing side was determined using a visual spot-checking method. The chewing rate was determined as the average of two chewing cycles. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. The data of the subjects were diagnosed by the dentist. In this study, the fast Fourier transform (FFT) analysis was applied to sEMG signals recorded from subjects. The number of FFT coefficients had been selected by using Logistic Regression (LR) methodology. Then the ability of a multilayer perceptron artificial neural network (MLPANN) in the diagnosis of neuromuscular disorders in investigated. To find the best neuron weights and structures for MLPANN, particle swarm optimization (PSO) was utilized. Results showed the proficiency of the suggested diagnostic system for the classification of EMG signals. The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite.

Keywords

20.1001.1.23225211.2022.10.2.1.1

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Journal of AI and Data Mining

Classification of sEMG Signals for Diagnosis of Unilateral Posterior Crossbite in Primary Dentition using Fast Fourier Transform and Logistic Regression

References

References

Volume 10, Issue 2
April 2022
Pages 151-158

Classification of sEMG Signals for Diagnosis of Unilateral Posterior Crossbite in Primary Dentition using Fast Fourier Transform and Logistic Regression

References

References

Volume 10, Issue 2April 2022Pages 151-158

Volume 10, Issue 2
April 2022
Pages 151-158