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

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.

[1]  A. Al-hazimi,et al.  Time-domain analysis of heart rate variability in diabetic patients with and without autonomic neuropathy. , 2002, Annals of Saudi medicine.

[2]  Clodoaldo Ap. M. Lima,et al.  Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines , 2009, Expert Syst. Appl..

[3]  Gang Wang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..

[4]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures , 2015, Entropy.

[5]  T. Koenig,et al.  Event-Related Potential and EEG Measures in Parkinson’s Disease without and with Dementia , 2000, Dementia and Geriatric Cognitive Disorders.

[6]  João Paulo Papa,et al.  Improving Parkinson's disease identification through evolutionary-based feature selection , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  A. Korczyn,et al.  EEG in demented and non‐demented parkinsonian patients , 1988, Acta neurologica Scandinavica.

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[10]  Jihong Ouyang,et al.  An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach , 2014, Comput. Math. Methods Medicine.

[11]  C. M. Lim,et al.  Analysis of epileptic EEG signals using higher order spectra , 2009, Journal of medical engineering & technology.

[12]  U. Rajendra Acharya,et al.  Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features , 2012, Knowl. Based Syst..

[13]  Freddie Åström,et al.  A parallel neural network approach to prediction of Parkinson's Disease , 2011, Expert Syst. Appl..

[14]  F. Valldeoriola,et al.  Neurophysiological correlate of clinical signs in Parkinson's disease , 2002, Clinical Neurophysiology.

[15]  N. Birbaumer,et al.  Investigation of brain dynamics in Parkinson's disease by methods derived from nonlinear dynamics , 2001, Experimental Brain Research.

[16]  J. Edgar,et al.  Gamma synchrony: Towards a translational biomarker for the treatment-resistant symptoms of schizophrenia , 2012, Neuropharmacology.

[17]  Francisco Sepulveda,et al.  Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface , 2008, Inf. Sci..

[18]  Y. Parmet,et al.  EEG frequency analysis in demented and nondemented parkinsonian patients. , 1994, Dementia.

[19]  Denis Kouame,et al.  New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices , 2014, Comput. Math. Methods Medicine.

[20]  U. Rajendra Acharya,et al.  Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound , 2012, Journal of Medical Systems.

[21]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method , 2015, Knowl. Based Syst..

[22]  E. Sullivan,et al.  Cognitive impairment in early, untreated Parkinson's disease and its relationship to motor disability. , 1991, Brain : a journal of neurology.

[23]  J. Suri,et al.  Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms , 2011, Technology in cancer research & treatment.

[24]  H. Soininen,et al.  Slowing of EEG in Parkinson's disease. , 1991, Electroencephalography and clinical neurophysiology.

[25]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[26]  M. Murugappan,et al.  Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst , 2013, BioMedical Engineering OnLine.

[27]  E. Růžička,et al.  Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson's disease , 2001, Clinical Neurophysiology.

[28]  Tong Liu,et al.  Author's Personal Copy Biomedical Signal Processing and Control Effective Detection of Parkinson's Disease Using an Adaptive Fuzzy K-nearest Neighbor Approach , 2022 .

[29]  Rakesh Jain,et al.  Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder , 2009, Psychiatry Research.

[30]  Kemal Polat,et al.  Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering , 2012, Int. J. Syst. Sci..

[31]  C. M. Lim,et al.  Application of higher order statistics/spectra in biomedical signals--a review. , 2010, Medical engineering & physics.

[32]  U. Rajendra Acharya,et al.  Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis , 2012, Journal of Medical Systems.

[33]  Joel E. W. Koh,et al.  A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals , 2015, European Neurology.

[34]  Arif Gülten,et al.  Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..

[35]  A. Vannucci,et al.  BICS Bath Institute for Complex Systems A note on time-dependent DiPerna-Majda measures , 2008 .

[36]  Nafiz Arica,et al.  Classification of Obsessive Compulsive Disorder by EEG Complexity and Hemispheric Dependency Measurements , 2015, Int. J. Neural Syst..

[37]  Olcay Kursun,et al.  Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia , 2010, Journal of Medical Systems.

[38]  Jiang Wang,et al.  Investigation of EEG abnormalities in the early stage of Parkinson’s disease , 2013, Cognitive Neurodynamics.

[39]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[40]  Dhanjoo N. Ghista,et al.  PHYSIOLOGICAL SYSTEMS' NUMBERS IN MEDICAL DIAGNOSIS AND HOSPITAL COST-EFFECTIVE OPERATION , 2004 .

[41]  U. Rajendra Acharya,et al.  Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra , 2010, Int. J. Neural Syst..

[42]  Akin Ozcift,et al.  SVM Feature Selection Based Rotation Forest Ensemble Classifiers to Improve Computer-Aided Diagnosis of Parkinson Disease , 2012, Journal of medical systems.

[43]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[44]  Nawwaf N. Kharma,et al.  Advances in Detecting Parkinson's Disease , 2010, ICMB.

[45]  Dhanjoo N. Ghista,et al.  NONDIMENSIONAL PHYSIOLOGICAL INDICES FOR MEDICAL ASSESSMENT , 2009 .

[46]  A. Hossen,et al.  Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals , 2010, Biomed. Signal Process. Control..

[47]  Wendy R. Sanhai,et al.  Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives , 2010, Nature Reviews Drug Discovery.

[48]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[49]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[50]  Theodoros Damoulas,et al.  Multiclass Relevance Vector Machines: Sparsity and Accuracy , 2010, IEEE Transactions on Neural Networks.

[51]  Mohammad Reza Daliri,et al.  Chi-square distance kernel of the gaits for the diagnosis of Parkinson's disease , 2013, Biomed. Signal Process. Control..

[52]  Ji-Wu Zhang,et al.  Bispectrum analysis of focal ischemic cerebral EEG signal using third-order recursion method , 2000, IEEE Transactions on Biomedical Engineering.

[53]  Max A. Little,et al.  Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.

[54]  Kenneth Sundaraj,et al.  Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[55]  Resul Das,et al.  A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..

[56]  U. Rajendra Acharya,et al.  Application of entropies for automated diagnosis of epilepsy using EEG signals: A review , 2015, Knowl. Based Syst..

[57]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

[58]  Hisashi Kobayashi,et al.  Probability, Random Processes, and Statistical Analysis: Random processes , 2011 .

[59]  Babak Shahbaba,et al.  Nonlinear Models Using Dirichlet Process Mixtures , 2007, J. Mach. Learn. Res..

[60]  WangGang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013 .

[61]  U. Rajendra Acharya,et al.  An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features , 2015, Knowl. Based Syst..

[62]  Der-Chiang Li,et al.  A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets , 2011, Artif. Intell. Medicine.