A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform

Şakar, C. Okan; Serbes, Görkem; Gündüz, Ayşegül; Tunç, Hünkar Can; Nizam, Hatice; Şakar, Betül Erdoğdu; Tütüncü, Melih; Aydın, Tarkan; Isenkul, M. Erdem; Apaydın, Hülya

doi:10.1016/j.asoc.2018.10.022

Cited by 382 publications

(338 citation statements)

References 31 publications

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“…Even when compared to the actually reported accuracies of these works (see Table 4), the results of the (XGBoost + mRMR) model is significantly better, even when the size of the selected dataset is way bigger than what were used in previous works. In comparison to most recent work done by Sakar et al [22] on the very dataset which is also used in our work, the results are comparatively better with a significant improvement in PD detection accuracy of 95.39% against 86.0% achieved in [22]. One of the profound reasons behind the performance on the XGBoost technique in developing an efficient model for the problem, was that it constructs several decision trees and finally aggregates the predictions made by each decision tree.…”

Section: Resultsmentioning

confidence: 63%

“…For PD detection, the different speech signal processing algorithms were compared by C.O Sakar et al [22]. In their work, a new feature was introduced called as tunable Q-factor wavelet transform (TQWT).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The initial step towards the classification is the collection of data. For the voice analysis, the data is collected from the UCI, a machine learning repository which contains the voice data for both PD and healthy subjects [22]. The dataset used consists of 756 instances and 754 attributes.…”

Section: Pd Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

Nissar¹,

Rizvi²,

Masood³

et al. 2019

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning (ML) has helped in solving problems of computer vision, natural language processing, speech recognition etc.OBJECTIVES: This paper aims to analyse the effect of feature type selection i.e. MFCC and TQWT on the efficiency of voice based PD detection system along with the use an ensemble learning based classifier for this task.METHODS: Hence, in this work, various machine learning models, including Logistic Regression, Naive Bayes, KNN, Random Forest, Decision Tree, SVM, MLP, and XGBoost, have been employed and explored for PD detection purpose. The task of Feature selection was also done using minimum-Redundancy and Maximum-Relevance (mRMR) and Recursive Feature Elimination (RFE) techniques. RESULTS:The results of the XGBoost with mRMR feature selection, outperformed all other models with a high accuracy of 95.39% and precision, recall and F1-score of 0.95 each, when both MFCC and TQWT features were selected. CONCLUSION:The results obtained strongly support the use of XGBoost model for the voice sample based detection of PD along with mRMR feature selection technique.

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Section: Resultsmentioning

confidence: 63%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

Nissar¹,

Rizvi²,

Masood³

et al. 2019

EAI Endorsed Transactions on Pervasive Health and Technology

View full text Add to dashboard Cite

show abstract

“…Discrete Wavelet Transform (DWT) has been adopted for a huge variety of applications, from speaker recognition [4] to Parkinson's disease detection [30]. Briefly, DWT is a time-scale representation technique that iteratively transforms the input signal into multi-resolution subsets of coefficients through high-pass and low-pass filters and decimation operators.…”

Section: Wavelet Based Feature Extraction Methodsmentioning

confidence: 99%

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

2019

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Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines.

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“…The vocal impairment is considered one of the earliest indications that this disease begins. In this line, recent works have endeavored in handling voice signals to support the diagnosis [22,37]. In this work, we forward another step in the study of automatic classification of complex diseases by improving the learning machine.…”

Section: Parkinson's Diseasementioning

confidence: 99%

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

et al. 2020

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During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform

Cited by 382 publications

References 31 publications

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

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