Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

Tsanas, Athanasios; Little, Max A.; McSharry, Patrick E.; Spielman, Jennifer; Ramig, Lorraine O.

doi:10.1109/tbme.2012.2183367

Cited by 612 publications

(424 citation statements)

References 34 publications

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“…In this paper extended database is used with more features. Four feature selection algorithms LASSO, Mrmr, RELIEF, and LLBFS are used with two statistical classifier: random forest and support vector machine [12]. In this paper, we have used the feature dataset of Parkinson disease.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…This works continues a previous research using lab-quality digital audio recordings of sustained phonations [492]- [498]. Furthermore, they were able to accurately predict the severity of PD symptoms using the UPDRS [493].…”

Section: Project Descriptionsupporting

confidence: 82%

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

González¹

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“…Combinations of time-domain and frequency domain features have also been used for rating the severity of Parkinson's [16].…”

Section: Features For Detecting Health Changesmentioning

confidence: 99%

It Sounds Like You Have a Cold! Testing Voice Features for the Interspeech 2017 Computational Paralinguistics Cold Challenge

Huckvale

Beke

2017

Interspeech 2017

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This paper describes an evaluation of four different voice feature sets for detecting symptoms of the common cold in speech as part of the Interspeech 2017 Computational Paralinguistics Challenge. The challenge corpus consists of 630 speakers in three partitions, of which approximately one third had a "severe" cold at the time of recording. Success on the task is measured in terms of unweighted average recall of cold/not-cold classification from short extracts of the recordings. In this paper we review previous voice features used for studying changes in health and devise four basic types of features for evaluation: voice quality features, vowel spectra features, modulation spectra features, and spectral distribution features. The evaluation shows that each feature set provides some useful information to the task, with features from the modulation spectrogram being most effective. Feature-level fusion of the feature sets shows small performance improvements on the development test set. We discuss the results in terms of the most suitable features for detecting symptoms of cold and address issues arising from the design of the challenge.

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Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

Cited by 612 publications

References 34 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

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

It Sounds Like You Have a Cold! Testing Voice Features for the Interspeech 2017 Computational Paralinguistics Cold Challenge

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