Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Drissi, Taoufiq Belhoussine; Soumaya, Zayrit; Nsiri, Benayad; Ammoummou, Abdelkrim

doi:10.14569/ijacsa.2019.0100315

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…The use of MFCCs for PD detection was introduced in Tsanas et al (2012). Since then, many studies have used MFCCs for PD detection (Arias-Vergara et al, 2017;Naranjo et al, 2017;Vaiciukynas et al, 2017;Drissi et al, 2019;Fang et al, 2020) or PD monitoring (Grosz et al, 2015;Schuller et al, 2015;Orozco-Arroyave et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Jeancolas

Petrovska‐Delacrétaz

Mangone

et al. 2021

Front. Neuroinform.

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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).

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Section: Introductionmentioning

confidence: 99%

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Jeancolas

Petrovska‐Delacrétaz

Mangone

et al. 2021

Front. Neuroinform.

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“…Wavelet analysis is one of the powerful tools in signal processing, it is considered as a technique which aims to solve the problem of non-stationary signals. This notion was introduced in the 20th century by Haar who constructed the simplest wavelet, and then developed in the 1980s through the research work of Mallat [18], [19], [29]. One of the powerful things of this approach is that it allows a signal to be analyzed in time and frequency, which makes it very useful in extracting the various information contained in that signal.…”

Section: Preliminaries 21 Wavelet Analysismentioning

confidence: 99%

“…The purpose of this method is to separate the features of the signal into different categories, it's based on the construction of a separation area between the different classes of the learning set{𝑥 𝑖 , 𝑦 𝑖 }, with 𝑦 𝑖 ∈ {−1,1} and 𝑖 = 1,2, … . , 𝑛 the lines that delimit this area are called a hyperplane which is defined by [28], [29]:…”

Section: Classificationmentioning

confidence: 99%

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

Toulni

Nsiri

Taoufiq

2021

IJ-AI

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The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%.

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“…The wavelet transform has been used to tackle the problem of constant resolution. In the paper, Drissi et al [4] applied the different sorts of discrete wavelet transforms (DWT) on the speech signal to obtain the mel frequency cepstral coefficient (MFCC), then classified those coefficients by using the support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

“…In research [5], [6], authors used the Daubechies level 2 in the 3rd scale that gave the best results in [4] to extract the MFCC with two kernels of SVM Linear and radial basis function (RBF). Accuracy has been obtained by the RBF kernel while in the article [7].…”

Section: Introductionmentioning

confidence: 99%

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Nsiri

Soumaya

Taoufiq³

et al. 2022

IJECE

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<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

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Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Cited by 16 publications

References 27 publications

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

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