Early detection of Alzheimer's disease from EEG signals using Hjorth parameters

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%.

Automated summary

This study successfully enhanced the accuracy of early detection of AD from EEG signals by combining Hjorth parameters with other common features. The highest accuracy was achieved using the DWT method for signal decomposition and the KNN algorithm for classification.