The power features of Masseter muscle activity in tension-type and migraine without aura headache during open-close clench cycles

Abstract

IntroductionDifferent types of headaches and TMJ click influence the masseter muscle activity. The aim of this study was to assess the trend of energy level of the electromyography (EMG) activity of the masseter muscle during open-close clench cycles in migraine without aura (MOA) and tension-type headache (TTH) with or without TMJ click.MethodsTwenty-five women with MOA and twenty four women with TTH participated in the study. They matched with 25 healthy subjects, in terms of class of occlusion and prevalence of temporomandibular joint (TMJ) with click. The EMG of both masseter muscles were recorded during open-close clench cycles at a rate of 80 cycles per minute for 15 seconds. The mouth opening was restricted to two centimeters by mandibular motion frame. Signal processing steps have been done on the EMG as: noise removing, smoothing, feature extraction, and statistical analyzing. The six statistical parameters of energy computed were mean, Variance, Skewness, Kurtosis, and first and second half energy over all signal energy.ResultsA three-way ANOVA indicated that during all the cycles, the mean of energy was more and there was a delay in showing the peak of energy in the masseter of the left side with clicked TMJ in MOA group compared to the two other groups, while this pattern occurred inversely in the side with no-clicked TMJ (P < 0.009). The variation of energy was significantly less in MOA group compared to the two other groups in the no-clicked TMJ (P < 0.003). However, the proportion of the first or second part of signal energy to all energy showed that TTH group had less energy in the first part and more energy in the second part in comparison to the two other groups (P < 0.05).ConclusionThe study showed different changes in the energy distribution of masseter muscle activity during cycles in MOA and TTH. MOA, in contrast to TTH, had lateralization effect on EMG and interacted with TMJ click.

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

“…This phenomenon is known as the curse of dimensionality, where increasing the number of features cannot guarantee performance improvement and may even lead to performance decay. Therefore, that phenomenon should be avoided as much as possible to maintain the classifier performance at a satisfactory level (Keogh & Mueen, 2011; Alizadeh Savareh et al, 2017). …”

Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation

“…Its main capability is to parse input signal at different scales of details and approximations. So, it is known as a multispectral analysis tool, capable of extracting information from signals at different levels [ 41 – 45 ]. Using wavelet transforms to enhance the CNN performance requires applying some considerations such as the type of kernel function and the number of wavelet decomposition levels that are the subject of the present study.…”