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

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Abstract

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.

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Acknowledgements

The authors would like to thank all the individuals who supported and participated in this study.

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  1. Rajamanickam Yuvaraj

    Present address: Neural Systems Laboratory, Department of Biomedical Engineering, University of Kentucky, 143 Graham Ave, Lexington, KY, 40506-0108, USA

Authors and Affiliations

  1. Department of Biomedical Engineering, Sri Sivasubramaniya Nadar (SSN) College of Engineering, Rajiv Gandhi Salai, Kalavakkam, Tamilnadu, 603110, India

    Rajamanickam Yuvaraj

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore

    U. Rajendra Acharya & Yuki Hagiwara

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  1. Rajamanickam Yuvaraj
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Correspondence to Rajamanickam Yuvaraj.

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Yuvaraj, R., Rajendra Acharya, U. & Hagiwara, Y. A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Comput & Applic 30, 1225–1235 (2018). https://doi.org/10.1007/s00521-016-2756-z

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