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Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning

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Abstract

Machine learning (ML) and Deep learning (DL) methods are differently implemented with various decision-making abilities. Particularly, the usage of ML and DL techniques in disease detection is inevitable in the near future. This work uses the ability of acoustic-based DL techniques for detecting Parkinson disease symptoms. This disease can be identified by many DL techniques such as deep knowledge creation networks and recurrent networks. The proposed Deep Multi-Variate Vocal Data Analysis (DMVDA) System has been designed and implemented using Acoustic Deep Neural Network (ADNN), Acoustic Deep Recurrent Neural Network (ADRNN), and Acoustic Deep Convolutional Neural Network (ADCNN). Further, DMVDA has been specially developed with absolute multi-variate speech attribute processing algorithm for effective value creation. In order to improve the benefits of this speech-processing algorithm, the DMVDA has acoustic data sampling procedures. The DL techniques introduced in this work helps to identify Parkinson symptoms by analyzing the heterogeneous dataset. The integration of these techniques produces nominal 3% increase in the performance than the existing techniques.

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Abbreviations

A(M) :

Parkinson acoustic model

S(p) :

Speech sound pressure

M(v) :

Medium wave particle velocity

ϑ :

Sound vector

P(f) :

Adaptive frequency

λ T :

Wavelength

f :

Frequency

Mm :

Multimodal acoustic data matrix

T :

Time interval

C :

Number of classes

D(S) :

Speech dataset

τ :

Transition function

Rh(T) :

Recurrent hidden layer

U T :

Input at time ‘T’

ψ :

Output function

W :

State transition matrix

Z :

Input matrix

J :

Output matrix

Ф :

Nonlinear acoustic element function

G T :

Gate recurrent unit

s :

Data sample

u :

Normal distribution value

I :

Success rate

m :

Failure rate

S :

Size of population

α :

Sampling error rate

e :

Convolution function

ML:

Machine learning

DL:

Deep learning

DNN:

Deep neural network

RNN:

Recurrent neural network

CNN:

Convolutional neural network

PCA:

Principle component analysis

SVM:

Support vector machine

NN:

Neural network

RF:

Random Forest

NB:

Naïve Bayes

EEG:

Electroencephalogram

DMVDA:

Deep Multi-Variate Vocal Data Analysis

ADNN:

Acoustic Deep Neural Network

ADRNN:

Acoustic Deep Recurrent Neural Network

ADCNN:

Acoustic Deep Convolutional Neural Network

LSTM:

Long Short-Term Memory

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GGR College of Engineering, Vellore, India

    Gayathri Nagasubramanian

  2. Department of Information Technology, Thiagarajar College of Engineering, Madurai, India

    Muthuramalingam Sankayya

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  1. Gayathri Nagasubramanian
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  2. Muthuramalingam Sankayya
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Correspondence to Gayathri Nagasubramanian.

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Nagasubramanian, G., Sankayya, M. Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning. Neural Comput & Applic 33, 4849–4864 (2021). https://doi.org/10.1007/s00521-020-05233-7

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  • DOI: https://doi.org/10.1007/s00521-020-05233-7

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