A pattern recognition method for stage classification of Parkinson's disease utilizing voice features

Caesarendra, Wahyu; Ariyanto, Mochammad; Setiawan, Joga Dharma; Arozi, Moh.; Chang, Cindy R.

doi:10.1109/iecbes.2014.7047636

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Classification into different PD disease stages is performed after feature selection. Classifiers can be support vector machine (SVM), 12,15,16,18 linear or quadratic discriminant analysis, 17,18 naïve Bayes, 13 and so on. Deep learning, such as a convolutional neural network (CNN) framework, is another suitable method for predicting PD stages.…”

Section: Introductionmentioning

confidence: 99%

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

Huang

Lin

Cai

et al. 2020

Statistical Analysis

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We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

Huang

Lin

Cai

et al. 2020

Statistical Analysis

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“…The literature shows that a sizable number of researchers have made many attempts to classify people correctly as either PD patients or healthy people based on speech data. They are mostly based on different extracted features, feature selection/transformation methods [8,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], or classifiers [14-16, 20-22, 27, 30-35] to maximize the accuracy of classification of Parkinson's disease. As for feature extraction, the PD speech feature data primarily include pitch type, energy type, speed type, and content type [8,14,15].…”

Section: Introductionmentioning

confidence: 99%

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Zhang

et al. 2021

Neural Comput & Applic

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Speech diagnosis of Parkinson's disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.

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“…PD first describe by Dr James Parkinson in his paper titled "An Essay on The Shaking Palsy" published on 1871 [1]. PD is one of the top ten most common illnesses in Ciptomangunkusumo Hospital (RSCM) Indonesia [2]. PD is clinically detected by motoric symptom such as tremor, bradykinesia, rigidity and postural instability [1].…”

Section: Introductionmentioning

confidence: 99%

“…Earlier PD detection study utilizing voice features for pattern recognition methods has been presented in [2,6]. The 22 voice features used in those papers were obtained from the University of California-Irvine (UCI) data repository.…”

Section: Introductionmentioning

confidence: 99%

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Putri

Caesarendra

Pamanasari

et al. 2018

JEMMME

Self Cite

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Parkinson disease (PD) detection using pattern recognition method has been presented in literatures. This paper present multi-class PD detection utilizing voice and electromyography (EMG) features of Indonesian subjects. The multi-class classification consists of healthy control, possible stage, probable stage and definite stage. These stages are based on Hughes scale used in Indonesia for PD. Voice signals were recorded from 15 people with Parkinson (PWP) and 8 healthy control subjects. Voice and EMG data acquistion were conducted in dr Kariadi General Hospital Semarang, Central Java, Indonesia. Twenty two features are used for voice signal feature extraction and twelve features are emploed for EMG signal. Artificial Neural Network is used as classification method. The results of voice classification show that accuracy for testing step of 94.4%. For EMG classification, the accuracy of testing of 71%.

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A pattern recognition method for stage classification of Parkinson's disease utilizing voice features

Cited by 7 publications

References 13 publications

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

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