Disease detection using voice analysis: a review

Saloni,; Sharma, R. K.; Gupta, Anil Kumar

doi:10.1504/ijmei.2014.063173

Cited by 8 publications

(4 citation statements)

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“…It is a binary classifier and to do a multi-class classification, pair-wise classifications can be used. Cases where linear separation is not possible kernel functions like polynomial, RBF, Sigmoidal are used [15]. …”

Section: A Support Vector Machinesmentioning

confidence: 99%

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Sharma¹,

Gupta²

2015

IJISA

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Abstract-Parkinson is a neurological disease and occurs due to lack of dopamine neurons. These dopamine neurons manage all body movements. Parkinson patients have difficulty in doing all daily routine activities, and also have disturbed vocal fold movements. Using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. In this paper, we have used 23 features dataset, all the features are analyzed and 15 features are selected from the total dataset. As in Parkinson tremor is present in the voice box muscles, so the variation in the period and amplitude of consecutive vocal cycles is present. The feature dataset selected consist of jitter, shimmer, harmonic to noise ratio, DFA, spread1 and PPE. Various classifiers are used and their comparison is done to find out which classifier is perfect in this environment. It is concluded that support vector classifiers as the best one with an accuracy of 96%. In the neural network classifiers with different transfer functions, there is tradeoff among the performance parameters.

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Section: A Support Vector Machinesmentioning

confidence: 99%

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Sharma¹,

Gupta²

2015

IJISA

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“…The different methods of acoustic feature extraction and classification that can help in detecting the disease in the prior stage leading to the discrimination between the voice of healthy and unhealthy persons were discussed in (Saloni et al, 2014). Digital signal processing (DSP) techniques were used for feature extraction whereas vector quantization (VQ), DTW, SVM, GMM, and artificial neural network (ANN) were used for feature classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters

2023

Archives of Acoustics

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Non-invasive techniques for the assessment of respiratory disorders have gained increased importance in recent years due to the complexity of conventional methods. In the assessment of respiratory disorders, machine learning may play a very essential role. Respiratory disorders lead to variation in the production of speech as both go hand in hand. Thus, speech analysis can be a useful means for the pre-diagnosis of respiratory disorders. This article aims to develop a machine learning approach to differentiate healthy speech from speech corresponding to different respiratory disorders (affected). Thus, in the present work, a set of 15 relevant and efficient features were extracted from acquired data, and classification was done using different classifiers for healthy and affected speech. To assess the performance of different classifiers, accuracy, specificity (Sp), sensitivity (Se), and area under the receiver operating characteristic curve (AUC) was used by applying both multi-fold cross-validation methods (5-fold and 10-fold) and the holdout method. Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC > 82%, and accuracy > 99%). The conclusion was that the proposed classifiers may provide an aid in the simple assessment of respiratory disorders utilising speech parameters with high efficiency. In the future, the proposed approach can be evaluated for the detection of specific respiratory disorders such as asthma, COPD, etc.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Kin Fong Lei . using voices because some diseases directly affected human voice [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

et al. 2020

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Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.INDEX TERMS MCMTTP, discrete wavelet transform, voice disease detection, smart health, machine learning.

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Disease detection using voice analysis: a review

Cited by 8 publications

References 0 publications

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

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