A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

• Published in: Neural computing & applications Vol. 30; no. 4; pp. 1225 - 1235
• Main Authors: Yuvaraj, Rajamanickam, Rajendra Acharya, U., Hagiwara, Yuki
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2018; Springer Nature B.V
• Subjects: Abnormalities; Affective computing; Artificial Intelligence; Bayesian analysis; Biomedical materials; Classifiers; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Diagnosis; Electroencephalography; Epilepsy; Feature extraction; Image Processing and Computer Vision; Neural networks; Original Article; Probability and Statistics in Computer Science; Sleep; Support vector machines; Machine learning algorithms; Electroencephalogram; Higher-order spectra; Diagnosis index; Parkinson’s disease
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2756-z

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.