Diagnosis of Parkinson's disease from electroencephalography signals using linear and self‐similarity features

[1]  Marti A. Hearst.  Intelligent Connections: Battling with GA-Joe. , 1998 .

[2]  U. Rajendra Acharya,et al.  A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank , 2019, Pattern Recognit. Lett..

[3]  Manish Sharma,et al.  A NOVEL APPROACH FOR EPILEPSY DETECTION USING TIME–FREQUENCY LOCALIZED BI-ORTHOGONAL WAVELET FILTER , 2019, Journal of Mechanics in Medicine and Biology.

[4]  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.

[5]  Kenneth Sundaraj,et al.  Inter-hemispheric EEG coherence analysis in Parkinson’s disease: Assessing brain activity during emotion processing , 2015, Journal of Neural Transmission.

[6]  U. Rajendra Acharya,et al.  Dual-Tree Complex Wavelet Transform-Based Features for Automated Alcoholism Identification , 2018, International Journal of Fuzzy Systems.

[7]  Saman K. Halgamuge,et al.  Classification of Parkinson's Disease Gait Using Spatial-Temporal Gait Features , 2015, IEEE Journal of Biomedical and Health Informatics.

[8]  U. Rajendra Acharya,et al.  A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals , 2017, Applied Intelligence.

[9]  Clayton R. Pereira,et al.  A survey on computer-assisted Parkinson's Disease diagnosis , 2019, Artif. Intell. Medicine.

[10]  Arun Sharma,et al.  Diagnosis of Parkinson’s disease using modified grey wolf optimization , 2019, Cognitive Systems Research.

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

[12]  U. Rajendra Acharya,et al.  A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals , 2016, Neural Computing and Applications.

[13]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[14]  U. Rajendra Acharya,et al.  A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank , 2018, Comput. Biol. Medicine.

[15]  F. Galton.  I. Co-relations and their measurement, chiefly from anthropometric data , 1889, Proceedings of the Royal Society of London.

[16]  D. O'Shaughnessy,et al.  Linear predictive coding , 1988, IEEE Potentials.

[17]  N. Jetté,et al.  The prevalence of Parkinson's disease: A systematic review and meta‐analysis , 2014, Movement disorders : official journal of the Movement Disorder Society.

[18]  G. Rizzo,et al.  Accuracy of clinical diagnosis of Parkinson disease , 2016, Neurology.

[19]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.

[20]  Aboul Ella Hassanien,et al.  Improved diagnosis of Parkinson's disease using optimized crow search algorithm , 2018, Comput. Electr. Eng..

[21]  Kenneth Sundaraj,et al.  On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing , 2014, Behavioral and Brain Functions.

[22]  Jonathan E. Fieldsend,et al.  Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases , 2006, Neural Computing and Applications.

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

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

[25]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[26]  H. Adeli,et al.  Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia , 2016, Behavioural Brain Research.

[27]  C. Adler,et al.  Quantitative EEG as a predictive biomarker for Parkinson disease dementia , 2011, Neurology.

[28]  Hojjat Adeli,et al.  Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network , 2015, Journal of Medical Systems.

[29]  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 .

[30]  Ram Bilas Pachori,et al.  Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters , 2017, Signal Process..

[31]  Chris J. Hass,et al.  Quantitative Normative Gait Data in a Large Cohort of Ambulatory Persons with Parkinson’s Disease , 2012, PloS one.

[32]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[33]  U. Rajendra Acharya,et al.  A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension , 2017, Pattern Recognit. Lett..

[34]  Ram Bilas Pachori,et al.  A NOVEL APPROACH TO DETECT EPILEPTIC SEIZURES USING A COMBINATION OF TUNABLE-Q WAVELET TRANSFORM AND FRACTAL DIMENSION , 2017 .

[35]  Ram Bilas Pachori,et al.  A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases , 2017, Signal Process..

[36]  U. Rajendra Acharya,et al.  A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals , 2019, Informatics in Medicine Unlocked.

[37]  U. Rajendra Acharya,et al.  MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection , 2018, Knowl. Based Syst..

[38]  U. Rajendra Acharya,et al.  Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters , 2019, Neural Computing and Applications.

[39]  U. Rajendra Acharya,et al.  An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank , 2018, Comput. Biol. Medicine.

[40]  M. Hariharan,et al.  A new hybrid intelligent system for accurate detection of Parkinson's disease , 2014, Comput. Methods Programs Biomed..

[41]  U. Rajendra Acharya,et al.  Parkinson's disease: Cause factors, measurable indicators, and early diagnosis , 2018, Comput. Biol. Medicine.

[42]  Kun Nie,et al.  The patterns of EEG changes in early-onset Parkinson's disease patients , 2017, The International journal of neuroscience.

[43]  Manish Sharma,et al.  Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals , 2018, Comput. Biol. Medicine.

[44]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[45]  R. Hilker,et al.  Arm swing asymmetry in Parkinson's disease measured with ultrasound based motion analysis during treadmill gait. , 2012, Gait & posture.

[46]  Deepak Joshi,et al.  An automatic non-invasive method for Parkinson's disease classification , 2017, Comput. Methods Programs Biomed..

[47]  J. Duysens,et al.  Quantitative gait analysis in patients with vascular parkinsonism , 1996, Movement disorders : official journal of the Movement Disorder Society.

[48]  Xiangyang Wang,et al.  Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson's Disease , 2017, Parkinson's disease.

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

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

[51]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[52]  U. Rajendra Acharya,et al.  A novel machine learning approach for early detection of hepatocellular carcinoma patients , 2019, Cognitive Systems Research.

[53]  U. Rajendra Acharya,et al.  An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals , 2019, Cognitive Systems Research.

[54]  Pawel Plawiak.  An estimation of the state of consumption of a positive displacement pump based on dynamic pressure or vibrations using neural networks , 2014, Neurocomputing.

[55]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

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

[57]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[58]  Jin-Tai Yu,et al.  The role of the LRRK2 gene in Parkinsonism , 2014, Molecular Neurodegeneration.