Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data

Parkinson disease (PD)'s speech recognition is an effective way for its diagnosis, which has become a hot and difficult research area in recent years. As we know, there are large corpuses (segments) within one subject. However, too large segments will increase the complexity of the classification model. Besides, the clinicians interested in finding diagnostic speech markers that reflect the pathology of the whole subject. Since the optimal relevant features of each speech sample segment are different, it is difficult to find the uniform diagnostic speech markers. Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject. To address this problem, an enveloped deep speech sample learning algorithm for Parkinson's subjects based on multilayer fuzzy c-mean (MlFCM) clustering and interlayer consistency preservation is proposed in this paper. The algorithm can be used to achieve intra-subject sample reconstruction for Parkinson's disease (PD) to obtain a small number of high-quality prototype sample segments. At the end of the paper, several representative PD speech datasets are selected and compared with the state-of-the-art related methods, respectively. The experimental results show that the proposed algorithm has higher recognition accuracy and better generalization performance. The proposed method in this paper can effectively reconstruct all speech sample segments within Parkinson's subjects and obtain a small number of prototype samples reflecting the overall pathological status of the subjects. This not only facilitates the extraction of speech features (diagnostic markers) reflecting the overall pathological status of the subjects, but also significantly improves the classification accuracy and generalization performance, and better meets the requirements of clinical applications.

[1]  Y. Zhang.  Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation , 2017, Parkinson's disease.

[2]  Wahyu Caesarendra,et al.  A pattern recognition method for stage classification of Parkinson's disease utilizing voice features , 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).

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

[4]  Zayrit Soumaya,et al.  The detection of Parkinson disease using the genetic algorithm and SVM classifier , 2021 .

[5]  Ahmed Hammouch,et al.  Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease , 2017 .

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

[7]  Yongming Li,et al.  Local discriminant preservation projection embedded ensemble learning based dimensionality reduction of speech data of Parkinson's disease , 2021, Biomed. Signal Process. Control..

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

[9]  Jianhua Gu,et al.  A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease , 2017, IEEE Access.

[10]  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).

[11]  Hananel Hazan,et al.  Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques , 2014, 2014 IEEE International Conference on Software Science, Technology and Engineering.

[12]  Hasan Demirel,et al.  Performance analysis of different classification algorithms using different feature selection methods on Parkinson's disease detection , 2018, Journal of Neuroscience Methods.

[13]  P. Alam.  ‘Z’ , 2021, Composites Engineering: An A–Z Guide.

[14]  Sai-Ho Ling,et al.  An Efficient Diagnosis System for Parkinson's Disease Using Deep Belief Network , 2017 .

[15]  이화영.  X , 1960, Chinese Plants Names Index 2000-2009.

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

[17]  J. Jankovic,et al.  Early diagnosis and therapy of Parkinson’s disease: can disease progression be curbed? , 2012, Journal of Neural Transmission.

[18]  Alexandre Mendes,et al.  Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction , 2018, PloS one.

[19]  Ce Zhu,et al.  Automated Detection of Parkinson’s Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network , 2019, IEEE Journal of Translational Engineering in Health and Medicine.

[20]  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.

[21]  Derya Avci,et al.  An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine , 2016, Parkinson's disease.

[22]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[23]  K. Forrest,et al.  The Impact of Lexical Characteristics and Noise on Intelligibility of Parkinsonian Speech. , 2018, Journal of speech, language, and hearing research : JSLHR.

[24]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[25]  Chi-Man Vong,et al.  Multinomial Bayesian extreme learning machine for sparse and accurate classification model , 2021, Neurocomputing.

[26]  Xiao Huang,et al.  Empirical Bayes Transfer Learning for Uncertainty Characterization in Predicting Parkinson’s Disease Severity , 2018, IISE Transactions on Healthcare Systems Engineering.

[27]  John Durkin,et al.  Expert Systems , 1994 .

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

[29]  Changqin Quan,et al.  A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech , 2021, IEEE Access.

[30]  Paavo Alku,et al.  The Detection of Parkinson's Disease From Speech Using Voice Source Information , 2021, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[31]  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.

[32]  B. Bloem,et al.  Being in control of Parkinson’s disease: A qualitative study of community-dwelling patients’ coping with changes in care , 2018, The European journal of general practice.

[33]  H. Walden,et al.  Parkin function in Parkinson's disease , 2018, Science.

[34]  P. Alam,et al.  H , 1887, High Explosives, Propellants, Pyrotechnics.

[35]  Yinggao Yue,et al.  Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application , 2021, IEEE Access.

[36]  Anil K. Jain,et al.  Artificial neural networks for feature extraction and multivariate data projection , 1995, IEEE Trans. Neural Networks.

[37]  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.

[38]  António M. Lopes,et al.  A Comparison of Classification Methods for Telediagnosis of Parkinson's Disease , 2016, Entropy.

[39]  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.

[40]  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, Arabian Journal for Science and Engineering.

[41]  Ping Wang,et al.  Simultaneous learning of speech feature and segment for classification of Parkinson disease , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[42]  Shivajirao M. Jadhav,et al.  Feature Ensemble Learning Based on Sparse Autoencoders for Diagnosis of Parkinson’s Disease , 2018, Advances in Intelligent Systems and Computing.

[43]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[44]  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.

[45]  M. Stern.  Parkinson's disease: early diagnosis and management. , 1993, The Journal of family practice.

[46]  Sridhar Krishnan,et al.  Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease , 2014, PloS one.

[47]  P. Alam.  ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[48]  Jirí Mekyska,et al.  Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease , 2016, Comput. Methods Programs Biomed..

[49]  Ina Kodrasi,et al.  Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection , 2020, IEEE/ACM Transactions on Audio, Speech, and Language Processing.