1
|
Kim H, Kim S, Lee S, Lee K, Kim E. Exploring the Relationships Between Antipsychotic Dosage and Voice Characteristics in Relation to Extrapyramidal Symptoms. Psychiatry Investig 2024; 21:822-831. [PMID: 39111750 PMCID: PMC11321868 DOI: 10.30773/pi.2023.0417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/19/2024] [Accepted: 04/09/2024] [Indexed: 08/15/2024] Open
Abstract
OBJECTIVE Extrapyramidal symptoms (EPS) are common side effects of antipsychotic drugs. Despite the growing interest in exploring objective biomarkers for EPS prevention and the potential use of voice in detecting clinical disorders, no studies have demonstrated the relationships between vocal changes and EPS. Therefore, we aimed to determine the associations between voice changes and antipsychotic dosage, and further investigated whether speech characteristics could be used as predictors of EPS. METHODS Forty-two patients receiving or expected to receive antipsychotic drugs were recruited. Drug-induced parkinsonism of EPS was evaluated using the Simpson-Angus Scale (SAS). Participants' voice data consisted of 16 neutral sentences and 2 second-long /Ah/utterances. Thirteen voice features were extracted from the obtained voice data. Each voice feature was compared between groups categorized based on SAS total score of below and above "0.6." The associations between antipsychotic dosage and voice characteristics were examined, and vocal trait variations according to the presence of EPS were explored. RESULTS Significant associations were observed between specific vocal characteristics and antipsychotic dosage across both datasets of 1-16 sentences and /Ah/utterances. Notably, Mel-Frequency Cepstral Coefficients (MFCC) exhibited noteworthy variations in response to the presence of EPS. Specifically, among the 13 MFCC coefficients, MFCC1 (t=-4.47, p<0.001), MFCC8 (t=-4.49, p<0.001), and MFCC12 (t=-2.21, p=0.029) showed significant group differences in the overall statistical values. CONCLUSION Our results suggest that MFCC may serve as a predictor of detecting drug-induced parkinsonism of EPS. Further research should address potential confounding factors impacting the relationship between MFCC and antipsychotic dosage, possibly improving EPS detection and reducing antipsychotic medication side effects.
Collapse
Affiliation(s)
- Hyeyoon Kim
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seoyoung Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Subin Lee
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Kyogu Lee
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Euitae Kim
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea
| |
Collapse
|
2
|
Ali L, Javeed A, Noor A, Rauf HT, Kadry S, Gandomi AH. Parkinson's disease detection based on features refinement through L1 regularized SVM and deep neural network. Sci Rep 2024; 14:1333. [PMID: 38228772 PMCID: PMC10791701 DOI: 10.1038/s41598-024-51600-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/07/2024] [Indexed: 01/18/2024] Open
Abstract
In previous studies, replicated and multiple types of speech data have been used for Parkinson's disease (PD) detection. However, two main problems in these studies are lower PD detection accuracy and inappropriate validation methodologies leading to unreliable results. This study discusses the effects of inappropriate validation methodologies used in previous studies and highlights the use of appropriate alternative validation methods that would ensure generalization. To enhance PD detection accuracy, we propose a two-stage diagnostic system that refines the extracted set of features through [Formula: see text] regularized linear support vector machine and classifies the refined subset of features through a deep neural network. To rigorously evaluate the effectiveness of the proposed diagnostic system, experiments are performed on two different voice recording-based benchmark datasets. For both datasets, the proposed diagnostic system achieves 100% accuracy under leave-one-subject-out (LOSO) cross-validation (CV) and 97.5% accuracy under k-fold CV. The results show that the proposed system outperforms the existing methods regarding PD detection accuracy. The results suggest that the proposed diagnostic system is essential to improving non-invasive diagnostic decision support in PD.
Collapse
Affiliation(s)
- Liaqat Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan
| | - Ashir Javeed
- Aging Research Center, Karolinska Institutet, Solna, Sweden
| | - Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, 80221, Jeddah, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, ST4 2DE, UK
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
| |
Collapse
|
3
|
Ngo QC, Motin MA, Pah ND, Drotár P, Kempster P, Kumar D. Computerized analysis of speech and voice for Parkinson's disease: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107133. [PMID: 36183641 DOI: 10.1016/j.cmpb.2022.107133] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity. METHODS A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed. RESULTS Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians. CONCLUSIONS The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.
Collapse
Affiliation(s)
| | - Mohammod Abdul Motin
- Biosignals Lab, RMIT University, Melbourne, Australia; Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Nemuel Daniel Pah
- Biosignals Lab, RMIT University, Melbourne, Australia; Universitas Surabaya, Indonesia
| | - Peter Drotár
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001, Kosice, Slovakia
| | - Peter Kempster
- Neurosciences Department, Monash Health, Clayton, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Dinesh Kumar
- Biosignals Lab, RMIT University, Melbourne, Australia.
| |
Collapse
|
4
|
Kaplan E, Altunisik E, Ekmekyapar Firat Y, Datta Barua P, Dogan S, Baygin M, Burak Demir F, Tuncer T, Palmer E, Tan RS, Yu P, Soar J, Fujita H, Rajendra Acharya U. Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107030. [PMID: 35878484 DOI: 10.1016/j.cmpb.2022.107030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/06/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. METHODS Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). RESULTS Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. SIGNIFICANCE The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.
Collapse
Affiliation(s)
- Ela Kaplan
- Department of Radiology, Adıyaman Training and Research Hospital, Turkey
| | - Erman Altunisik
- Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey
| | | | - Prabal Datta Barua
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Fahrettin Burak Demir
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Bandirma, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia; Discipline of Paediatrics and Child Health, School of Clinical Medicine Randwick, Faculty of Medicine and Health, UNSW, Randwick, NSW 2031, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Ping Yu
- School of Computing and Information Technology, University of Wollongong, Wollongong NSW 2522, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University of Technology, Ho Chi Minh City, Viet Nam; Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - U Rajendra Acharya
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
| |
Collapse
|
5
|
Bao G, Lin M, Sang X, Hou Y, Liu Y, Wu Y. Classification of Dysphonic Voices in Parkinson's Disease with Semi-Supervised Competitive Learning Algorithm. BIOSENSORS 2022; 12:502. [PMID: 35884305 PMCID: PMC9312485 DOI: 10.3390/bios12070502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann−Whitney−Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.
Collapse
|
6
|
Liu Y, Liu Z, Luo X, Zhao H. Diagnosis of Parkinson's disease based on SHAP value feature selection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
7
|
Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06626-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
8
|
Ali L, Chakraborty C, He Z, Cao W, Imrana Y, Rodrigues JJPC. A novel sample and feature dependent ensemble approach for Parkinson’s disease detection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07046-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractParkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.
Collapse
|
9
|
Saeed F, Al-Sarem M, Al-Mohaimeed M, Emara A, Boulila W, Alasli M, Ghabban F. Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods. COMPUTERS, MATERIALS & CONTINUA 2022; 71:5639-5658. [DOI: 10.32604/cmc.2022.023124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/19/2021] [Indexed: 06/15/2023]
|
10
|
|
11
|
Naranjo L, Pérez CJ, Campos-Roca Y, Madruga M. Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings. Artif Intell Med 2021; 120:102162. [PMID: 34629154 DOI: 10.1016/j.artmed.2021.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/21/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.
Collapse
Affiliation(s)
- Lizbeth Naranjo
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Carlos J Pérez
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain.
| | - Yolanda Campos-Roca
- Departamento de Tecnologías de los Computadores y de las Comunicaciones, Escuela Politécnica, Universidad de Extremadura, 10003 Cáceres, Spain
| | - Mario Madruga
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain
| |
Collapse
|
12
|
Er MB, Isik E, Isik I. Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
|
13
|
Gene Correlation Guided Gene Selection for Microarray Data Classification. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6490118. [PMID: 34435048 PMCID: PMC8382518 DOI: 10.1155/2021/6490118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/09/2021] [Indexed: 12/14/2022]
Abstract
The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.
Collapse
|
14
|
Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11157149] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.
Collapse
|
15
|
Jeancolas L, Petrovska-Delacrétaz D, Mangone G, Benkelfat BE, Corvol JC, Vidailhet M, Lehéricy S, Benali H. X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech. Front Neuroinform 2021; 15:578369. [PMID: 33679361 PMCID: PMC7935511 DOI: 10.3389/fninf.2021.578369] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/18/2021] [Indexed: 01/18/2023] Open
Abstract
Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
Collapse
Affiliation(s)
- Laetitia Jeancolas
- Paris Brain Institute-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Laboratoire SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
| | | | - Graziella Mangone
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Badr-Eddine Benkelfat
- Laboratoire SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
| | - Jean-Christophe Corvol
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Marie Vidailhet
- Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Sorbonne University, Inserm, CNRS, Paris Brain Institute-ICM, Paris, France.,Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Department of Neuroradiology, Paris, France
| | - Habib Benali
- Department of Electrical & Computer Engineering, PERFORM Center, Concordia University, Montreal, QC, Canada
| |
Collapse
|
16
|
Machine Learning Methods with Decision Forests for Parkinson’s Detection. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020581] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%.
Collapse
|
17
|
Karabayir I, Goldman SM, Pappu S, Akbilgic O. Gradient boosting for Parkinson's disease diagnosis from voice recordings. BMC Med Inform Decis Mak 2020; 20:228. [PMID: 32933493 PMCID: PMC7493334 DOI: 10.1186/s12911-020-01250-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 09/08/2020] [Indexed: 12/18/2022] Open
Abstract
Background Parkinson’s Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. Method We used “Parkinson Dataset with Replicated Acoustic Features Data Set” from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson’s Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946–0.955 in 4-fold cross validation using only seven acoustic features. Conclusions Machine learning can accurately detect Parkinson’s disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson’s disease.
Collapse
Affiliation(s)
- Ibrahim Karabayir
- Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, CTRE #127, Maywood, IL, 60153, USA.,Kirklareli University, Kirklareli, Turkey
| | - Samuel M Goldman
- School of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Suguna Pappu
- Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA
| | - Oguz Akbilgic
- Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, CTRE #127, Maywood, IL, 60153, USA.
| |
Collapse
|
18
|
Ul Haq A, Li J, Memon MH, khan J, Ali Z, Abbas SZ, Nazir S. Recognition of the parkinson’s disease using a hybrid feature selection approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-200075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Jianping Li
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Muhammad Hammad Memon
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Jalaluddin khan
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Zafar Ali
- School of Computer Science and Engineering Southeast University, Nanjing, China
| | - Syed Zaheer Abbas
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Pakistan
| |
Collapse
|
19
|
Haq AU, Li JP, Khan J, Memon MH, Nazir S, Ahmad S, Khan GA, Ali A. Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2649. [PMID: 32384737 PMCID: PMC7249007 DOI: 10.3390/s20092649] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 04/23/2020] [Accepted: 04/25/2020] [Indexed: 12/26/2022]
Abstract
Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient's clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.
Collapse
Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.P.L.); or (J.K.); (M.H.M.)
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.P.L.); or (J.K.); (M.H.M.)
| | - Jalaluddin Khan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.P.L.); or (J.K.); (M.H.M.)
| | - Muhammad Hammad Memon
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (J.P.L.); or (J.K.); (M.H.M.)
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi 23500, Pakistan;
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O.Box. 151, Alkharj 11942, Saudi Arabia;
| | - Ghufran Ahmad Khan
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611731, China;
| | - Amjad Ali
- Department of Computer Science and Software Technology, University of Swat, Mingora 19130, Pakistan;
| |
Collapse
|
20
|
Polat K, Nour M. Parkinson disease classification using one against all based data sampling with the acoustic features from the speech signals. Med Hypotheses 2020; 140:109678. [PMID: 32197120 DOI: 10.1016/j.mehy.2020.109678] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/11/2020] [Accepted: 03/15/2020] [Indexed: 11/25/2022]
Abstract
Parkinson's disease (PD) is a long-term degenerative disease that primarily affects the motor system of the central nervous system. This disease is difficult to diagnose and is one of the common diseases in the public. In this paper, we have proposed a novel data sampling method for the classification of Parkinson disease based on the acoustic features from the speech signals. In the proposed data sampling method, the one against all (OGA) has been used to divide the dataset into five equal parts. With applying the OGA to the PD dataset having two classes (healthy and Parkinson disease), the minority and majority classes have been obtained. First of all, for healthy class in the dataset (first case), five equal partitions have been composed and then for PD class in the dataset (second case), five equal partitions have been composed. To classify the these all data partitions, we have used three different classifiers including the weighted k-NN (nearest neighbor), Logistic Regression (LR), and support vector machine with medium Gaussian kernel function. In order to evaluate the performance of the proposed hybrid models (the combination of classifiers and OGA based data sampling), the classification accuracy, the confusion matrix, and area under the Receiver Operating Characteristic (ROC) curve (AUC) have been used. While the LR, SVM with Gaussian, and weighted k-NN classifiers achieved the classification accuracies of 77.50%, 83.80%, and 82.10% in the classification of PD with the acoustic features, the combinations of classifiers and OGA based data sampling (first case) obtained the 79.04%, 87.36%, and 88.48% using the LR, SVM with Gaussian, and weighted k-NN classifiers, respectively. In the second case, the obtained classification accuracies are the 84.30%, 88.76%, and 89.46% using the LR, SVM with Gaussian, and weighted k-NN classifiers with the OGA based data sampling, respectively. The achieved results have shown that the proposed the one against all (OGA) based data sampling could be used in the combination of classifier algorithms as the data pre-processing method in the classification of Parkinson's disease with acoustic features.
Collapse
Affiliation(s)
- Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280 Bolu, Turkey.
| | - Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
| |
Collapse
|
21
|
Lesot MJ, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR. Hybrid Model for Parkinson’s Disease Prediction. INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS 2020. [PMCID: PMC7274681 DOI: 10.1007/978-3-030-50143-3_49] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Parkinson’s is a chronic, progressive neurological disease with no known cause that affects the central nervous system of older people and compromises their movement. This disorder can impair daily aspects of people and therefore identify their existence early, helps in choosing treatments that can reduce the impact of the disease on the patient’s routine. This work aims to identify Parkinson’s traces through a voice recording replications database applied to a fuzzy neural network to identify their patterns and enable the extraction of knowledge about situations present in the data collected in patients. The results obtained by the hybrid model were superior to state of the art for the theme, proving that it is possible to perform hybrid models in the extraction of knowledge and the classification of behavioral patterns of high accuracy Parkinson’s.
Collapse
Affiliation(s)
| | - Susana Vieira
- IDMEC, IST, Universidade de Lisboa, Lisbon, Portugal
| | | | | | - Anna Wilbik
- Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | | |
Collapse
|
22
|
Yaman O, Ertam F, Tuncer T. Automated Parkinson's disease recognition based on statistical pooling method using acoustic features. Med Hypotheses 2019; 135:109483. [PMID: 31954340 DOI: 10.1016/j.mehy.2019.109483] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/06/2019] [Accepted: 11/08/2019] [Indexed: 02/08/2023]
Abstract
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.
Collapse
Affiliation(s)
- Orhan Yaman
- Department of Informatics, Firat University, Elazig, Turkey.
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
| |
Collapse
|
23
|
Ali L, Zhu C, Zhang Z, Liu Y. Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:2000410. [PMID: 32166050 PMCID: PMC6876932 DOI: 10.1109/jtehm.2019.2940900] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/30/2019] [Accepted: 09/04/2019] [Indexed: 11/24/2022]
Abstract
Objective: Parkinson’s disease (PD) is a serious neurodegenerative disorder. It is
reported that most of PD patients have voice impairments. But these voice impairments are
not perceptible to common listeners. Therefore, different machine learning methods have
been developed for automated PD detection. However, these methods either lack
generalization and clinically significant classification performance or face the problem
of subject overlap. Methods: To overcome the problems discussed above, we attempt to
develop a hybrid intelligent system that can automatically perform acoustic analysis of
voice signals in order to detect PD. The proposed intelligent system uses linear
discriminant analysis (LDA) for dimensionality reduction and genetic algorithm (GA) for
hyperparameters optimization of neural network (NN) which is used as a predictive model.
Moreover, to avoid subject overlap, we use leave one subject out (LOSO) validation.
Results: The proposed method namely LDA-NN-GA is evaluated in numerical experiments on
multiple types of sustained phonations data in terms of accuracy, sensitivity,
specificity, and Matthew correlation coefficient. It achieves classification accuracy of
95% on training database and 100% on testing database using all the
extracted features. However, as the dataset is imbalanced in terms of gender, thus, to
obtain unbiased results, we eliminated the gender dependent features and obtained accuracy
of 80% for training database and 82.14% for testing database, which seems to
be more unbiased results. Conclusion: Compared with the previous machine learning methods,
the proposed LDA-NN-GA method shows better performance and lower complexity. Clinical
Impact: The experimental results suggest that the proposed automated diagnostic system has
the potential to classify PD patients from healthy subjects. Additionally, in future the
proposed method can also be exploited for prodromal and differential diagnosis, which are
considered challenging tasks.
Collapse
Affiliation(s)
- Liaqat Ali
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Ce Zhu
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Zhonghao Zhang
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Yipeng Liu
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| |
Collapse
|
24
|
Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning. Gene 2019; 706:188-200. [DOI: 10.1016/j.gene.2019.04.060] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 04/03/2019] [Accepted: 04/22/2019] [Indexed: 01/19/2023]
|
25
|
Naranjo L, Pérez CJ, Fuentes-García R, Martín J. A hidden Markov model addressing measurement errors in the response and replicated covariates for continuous nondecreasing processes. Biostatistics 2019; 21:743-757. [DOI: 10.1093/biostatistics/kxz004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 01/25/2019] [Accepted: 01/29/2019] [Indexed: 11/13/2022] Open
Abstract
Summary
Motivated by a study tracking the progression of Parkinson’s disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.
Collapse
Affiliation(s)
- Lizbeth Naranjo
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Del. Coyoacán, C.P. 04510 Ciudad de México, Mexico
| | - Carlos J Pérez
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, Avda. de la Universidad s/n, C.P. 10003 Cáceres, Spain
| | - Ruth Fuentes-García
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Del. Coyoacán, C.P. 04510 Ciudad de México, Mexico
| | - Jacinto Martín
- Departamento de Matemáticas, Facultad de Ciencias, Universidad de Extremadura, Avda. de Elvas s/n, C.P. 06006 Badajoz, Spain
| |
Collapse
|
26
|
Ryan P, Luz S, Albert P, Vogel C, Normand C, Elwyn G. Using artificial intelligence to assess clinicians' communication skills. BMJ 2019; 364:l161. [PMID: 30659013 DOI: 10.1136/bmj.l161] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Padhraig Ryan
- Centre of Health Policy and Management School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Pierre Albert
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Carl Vogel
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Charles Normand
- Centre of Health Policy and Management School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Glyn Elwyn
- Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire, USA
| |
Collapse
|
27
|
Abstract
This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.
Collapse
|