Deep dual-side learning ensemble model for Parkinson speech recognition
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Yuanfan Zhang | Pin Wang | Yongming Li | Jie Ma | Lang Zhou | Lingyun Qin | Yuwei Zeng | Yan Lei | Yongming Li | Pin Wang | Jie Ma | Lingyun Qin | Yan Lei | Yuanfan Zhang | Lang Zhou | Yuwei Zeng
[1] Akash Tayal,et al. Determination of Parkinson’s disease utilizing Machine Learning Methods , 2018, 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN).
[2] Max A. Little,et al. Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[3] A. Chakraborty,et al. Possible therapies of Parkinson’s disease: A review , 2020, Journal of Clinical Neuroscience.
[4] Elisabetta Farella,et al. Technology-Assisted Rehabilitation of Writing Skills in Parkinson's Disease: Visual Cueing versus Intelligent Feedback , 2017, Parkinson's disease.
[5] Omer Eskidere,et al. Detection of Parkinson's disease from vocal features using random subspace classifier ensemble , 2015, 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO).
[6] Musa Peker,et al. Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm. , 2015, Journal of healthcare engineering.
[7] Fikret S. Gürgen,et al. Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.
[8] Jian Yang,et al. Visual Representation and Classification by Learning Group Sparse Deep Stacking Network , 2018, IEEE Transactions on Image Processing.
[9] Alexandre Mendes,et al. Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction , 2018, PloS one.
[10] J. Paralič,et al. Parkinson's disease patients classification based on the speech signals , 2017, 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI).
[11] Y. Zhang. Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation , 2017, Parkinson's disease.
[12] Anil K. Jain,et al. Artificial neural networks for feature extraction and multivariate data projection , 1995, IEEE Trans. Neural Networks.
[13] Rabab Kreidieh Ward,et al. Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[14] F. Cavallo,et al. Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches , 2018, Annals of Biomedical Engineering.
[15] Ashkan Sami,et al. A Multiple-Classifier Framework for Parkinson's Disease Detection Based on Various Vocal Tests , 2016, International journal of telemedicine and applications.
[16] A. Sukesh Kumar,et al. Tablet PC Enabled Body Sensor System for Rural Telehealth Applications , 2016, International journal of telemedicine and applications.
[17] Max A. Little,et al. Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests , 2009, IEEE Transactions on Biomedical Engineering.
[18] M. Hariharan,et al. A new hybrid intelligent system for accurate detection of Parkinson's disease , 2014, Comput. Methods Programs Biomed..
[19] Paul McCrone,et al. Predicting the cost of Parkinson's disease , 2007, Movement disorders : official journal of the Movement Disorder Society.
[20] Ahmed Hammouch,et al. Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people , 2016, International Journal of Speech Technology.
[21] Aarushi Agarwal,et al. Prediction of Parkinson's disease using speech signal with Extreme Learning Machine , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).
[22] Max A. Little,et al. Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.
[23] Jiajie Peng,et al. Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder , 2019, Front. Genet..
[24] Gang Wang,et al. An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..
[25] Bart Baesens,et al. A multi-objective approach for profit-driven feature selection in credit scoring , 2019, Decis. Support Syst..
[26] Ahmed Hammouch,et al. Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease , 2017 .
[27] Yongming Li,et al. Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples , 2016, BioMedical Engineering OnLine.
[28] Fethullah Karabiber,et al. A Machine Learning System for the Diagnosis of Parkinson’s Disease from Speech Signals and Its Application to Multiple Speech Signal Types , 2016 .
[29] M. Dougherty,et al. Classification of speech intelligibility in Parkinson's disease , 2014 .
[30] J. Jankovic,et al. Early diagnosis and therapy of Parkinson’s disease: can disease progression be curbed? , 2012, Journal of Neural Transmission.
[31] Pawalai Kraipeerapun,et al. Using stacked generalization and complementary neural networks to predict Parkinson's disease , 2015, 2015 11th International Conference on Natural Computation (ICNC).
[32] Keh-Shih Chuang,et al. Dynamic feature selection for detecting Parkinson's disease through voice signal , 2015, 2015 IEEE MTT-S 2015 International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO).
[33] M. Stern. Parkinson's disease: early diagnosis and management. , 1993, The Journal of family practice.
[34] Zhizhong Mao,et al. Bagging ensemble of SVM based on negative correlation learning , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.
[35] Olcay Kursun,et al. Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia , 2010, Journal of Medical Systems.
[36] Martine Labbé,et al. Mixed Integer Linear Programming for Feature Selection in Support Vector Machine , 2018, Discret. Appl. Math..
[37] Max A. Little,et al. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007, Biomedical engineering online.
[38] A. Benba,et al. Hybridization of best acoustic cues for detecting persons with Parkinson's disease , 2014, 2014 Second World Conference on Complex Systems (WCCS).
[39] Max A. Little,et al. Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.