Alzheimer disorders diagnosis system design using machine learning for EEG signal

Year : 2023 | Volume : 14 | Issue : 01 | Page : 9-22
By

    Usha B. Patel

  1. Vandana V. Patel

  1. Research Scholar, Lalbhai Dalpatbhai College of Engineering, Gujarat, India
  2. Assistant Professor, Lalbhai Dalpatbhai College of Engineering, Gujarat, India

Abstract

The diagnosis of Alzheimer’s disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered signal was then divided into its frequency ranges using the Discrete Wavelet Transform (DWT) method, enabling the extraction of EEG signal characteristics. To enhance the diagnostic performance, various signal features were integrated into the DWT technique, including logarithmic band power (LBP), standard deviation, and root mean square (RMS). These features were incorporated to generate feature vectors that were utilized in the diagnosis process. In order to categorize the EEG features into their respective classes, various machine learning (ML) approaches were explored, including quadratic discriminant analysis (QDA), support vector machine (SVM), and k-nearest neighbour (KNN). The performance of these approaches was then evaluated by comparing and computing their sensitivity, specificiThe diagnosis of Alzheimer’s disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered signal was then divided into its frequency ranges using the Discrete Wavelet Transform (DWT) method, enabling the extraction of EEG signal characteristics. To enhance the diagnostic performance, various signal features were integrated into the DWT technique, including logarithmic band power (LBP), standard deviation, and root mean square (RMS). These features were incorporated to generate feature vectors that were utilized in the diagnosis process. In order to categorize the EEG features into their respective classes, various machine learning (ML) approaches were explored, including quadratic discriminant analysis (QDA), support vector machine (SVM), and k-nearest neighbour (KNN). The performance of these approaches was then evaluated by comparing and computing their sensitivity, specificity, and overall diagnosis accuracy. When comparing the accuracy values, it can be observed that QDA has a higher accuracy of 97.5% compared to KNN’s accuracy of 95%.ty, and overall diagnosis accuracy. When comparing the accuracy values, it can be observed that QDA has a higher accuracy of 97.5% compared to KNN’s accuracy of 95%.

Keywords: Alzheimer’s Disease, Electroencephalogram, Discrete Wavelet Transform, Logarithmic Band Power, Machine Learning.

[This article belongs to Journal of Control & Instrumentation(joci)]

How to cite this article: Usha B. Patel, Vandana V. Patel Alzheimer disorders diagnosis system design using machine learning for EEG signal joci 2023; 14:9-22
How to cite this URL: Usha B. Patel, Vandana V. Patel Alzheimer disorders diagnosis system design using machine learning for EEG signal joci 2023 {cited 2023 May 15};14:9-22. Available from: https://journals.stmjournals.com/joci/article=2023/view=110675

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Regular Issue Subscription Original Research
Volume 14
Issue 01
Received April 26, 2023
Accepted May 9, 2023
Published May 15, 2023