The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method
| Autor: | Milena Čukić, Dragoljub Donald Pokrajac, Miodrag Stokić, Slobodan Simić |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
business.industry Cognitive Neuroscience 05 social sciences food and beverages Pattern recognition Electroencephalography Logistic regression 050105 experimental psychology Random forest Support vector machine Sample entropy 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Polynomial kernel Multilayer perceptron Medicine 0501 psychology and cognitive sciences Artificial intelligence business 030217 neurology & neurosurgery Research Article |
| Zdroj: | Cogn Neurodyn |
| ISSN: | 1871-4080 |
| Popis: | Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi’s Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naive Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders. |
| Databáze: | OpenAIRE |
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