Biomedical Signal Processing and Control, Volume 65, March 2021,
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain that ultimately results in the death of neurons and dementia. The prevalence of the disease in the world is increasing rapidly. In recent years, many studies have been done to automatically detect this disease from brain signals. Method: In this paper, the Hjorth parameters are used along with other common features to improve the AD detection accuracy from EEG signals in early stages. Also different signal decomposition methods including filtering into brain frequency bands, discrete wavelet transform (DWT) and empirical mode decomposition (EMD), and various classification algorithms including support vector machine (SVM), K-nearest neighbors (KNN) and regularized linear discriminant analysis (RLDA) are evaluated. Results: After preprocessing and extracting the discriminative features from EEG signals for 35 healthy, 31 mild AD, and 20 moderate AD subjects, the performance of different decomposition methods and different classifiers was evaluated before and after combining Hjorth parameters. Conclusions: It was shown that combining Hjorth parameters to the common features improved the accuracy of detection and by using DWT method for signal decomposition and the KNN algorithm for classification the highest accuracy is obtained as 97.64%.
Adult; Alpha Rhythm; Alzheimer Disease; Alzheimer's Disease (AD); Article; Beta Rhythm; Biomedical Signal Processing; Classification Algorithm; Classifier; Clinical Article; Controlled Study; Cross Validation; Decomposition Methods; Delta Rhythm; Diagnostic Accuracy; Diagnostic Test Accuracy Study; Discrete Wavelet Transform; Discrete Wavelet Transform (DWT); Discrete Wavelet Transforms; Discriminant Analysis; Discriminative Features; Electroencephalogram; Electroencephalogram (EEG); Empirical Mode Decomposition; Empirical Mode Decomposition (EMD); Feature Extraction; Female; Gamma Rhythm; Hjorth Parameter; Hjorth Parameters; Human; K Nearest Neighbor; K Nearest Neighbor (KNN); K-nearest Neighbors (KNN); Learning Algorithms; Male; Multiclass Classification; Multiclass Support Vector Machine; Nearest Neighbor Search; Neurodegenerative Diseases; Neurodegenerative Disorders; Priority Journal; Regularized Linear Discriminant Analysis (R-LDA); Regularized Linear Discriminant Analysis (RLDA); Signal Decomposition; Signal Detection; Signal Distortion; Signal Processing; Signal Reconstruction; Spectroscopy; Support Vector Machine (SVM); Support Vector Machines; Theta Rhythm; Wavelet Decomposition; Global