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Frontiers in Biomedical Technologies، جلد ۹، شماره ۴، صفحات ۲۷۴-۲۸۲
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عنوان انگلیسی Predicting Mini-Mental State Examination Scores Using Electroencephalography Signal Features
چکیده انگلیسی مقاله Purpose: The purpose of this study is to use linear and non-linear features extracted from Electroencephalography (EEG) signal to predict the Mini-Mental State Examination (MMSE) test score by machine learning algorithms. Materials and Methods: First, the MMSE test was taken from 20 subjects that were referred with the initial diagnosis of dementia. Then, the brain activity of subjects was recorded via EEG signal. After preprocessing this signal, various linear and non-linear features are extracted from it that are used as input to machine learning algorithms to predict MMSE test scores in three levels. Results: Based on the experiments, the best classification result is related to the Long Short-Term Memory (LSTM) network with 68% accuracy. Conclusion: Findings show that by using machine learning algorithms and features extracted from EEG signal the MMSE scores are predicted in three levels. Although deep neural networks require a lot of data for training, the LSTM network has been able to achieve the best performance. By increasing the number of subjects, it is expected that the classification results will also increase.
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نویسندگان مقاله | Vahid Asayesh
Asab Pajouhane Farda Research Company, Tehran, Iran


| Mahdi Dehghani
Asayesh Rehabilitation Clinic, Tabriz, Iran


| Majid Torabi
Asab Pajouhane Farda Research Company, Tehran, Iran


| Sepideh Akhtari
Asab Pajouhane Farda Research Company, Tehran, Iran


| Shahriar Gharibzadeh
Institute for Cognitive and Brain Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
نشانی اینترنتی https://fbt.tums.ac.ir/index.php/fbt/article/view/395
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