Processing and Analysis of Human Voice for Assessment of Parkinson Disease

Saloni,; Sharma, Ratna; Gupta, AnilK.

doi:10.1166/jmihi.2016.1582

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Through extensive experiments, we demonstrated that a multimodal approach can improve neurological disease screening. Especially, adding voice features was effective for better generalization, which confirms existing reports on the importance of voice features 8 – 10 . From the single-protocol experiments, we observed that dynamic motions are more effective to screen stroke than simple ones.…”

Section: Conclusion and Discussionsupporting

confidence: 86%

“…Another equally effective piece of data is voice. It is also well known that voices are good indicators of neurological diseases 8 – 10 and can be recorded using the same video cameras. Voice data are one-dimensional data where the data dimension is even smaller than a sequence of landmarks; therefore, it is also efficient in terms of computational cost.…”

Section: Introductionmentioning

confidence: 99%

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A multimodal screening system for elderly neurological diseases based on deep learning

Park,

No,

Kim

et al. 2023

Sci Rep

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In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We proposed various examination protocols for screening neurological diseases and collected data by video-recording persons performing these protocols. We converted video data into human landmarks that capture action information with a much smaller data dimension. We also used voice data which are also effective indicators of neurological disorders. We designed a subnetwork for each protocol to extract features from landmarks or voice and a feature aggregator that combines all the information extracted from the protocols to make a final decision. Multitask learning was applied to screen two neurological diseases. To capture meaningful information about these human landmarks and voices, we applied various pre-trained models to extract preliminary features. The spatiotemporal characteristics of landmarks are extracted using a pre-trained graph neural network, and voice features are extracted using a pre-trained time-delay neural network. These extracted high-level features are then passed onto the subnetworks and an additional feature aggregator that are simultaneously trained. We also used various data augmentation techniques to overcome the shortage of data. Using a frame-length staticizer that considers the characteristics of the data, we can capture momentary tremors without wasting information. Finally, we examine the effectiveness of different protocols and different modalities (different body parts and voice) through extensive experiments. The proposed method achieves AUC scores of 0.802 for stroke and 0.780 for Parkinson’s disease, which is effective for a screening system.

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Section: Conclusion and Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

A multimodal screening system for elderly neurological diseases based on deep learning

Park,

No,

Kim

et al. 2023

Sci Rep

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“…As for feature transformation, the frequently used algorithms are PCA (principle component analysis) [26, 27, 31, 49]. As for feature selection, the frequently used algorithms are NN (neural network) based [27–30, 32, 49], serial search based [2, 14, 29, 31], random based [32, 33, 48], p value based [2, 27–34], relevance based [35, 36] or entropy based [37], discrimination algorithm (DA) based [47]. As for classifier design, the predominantly used classifiers include a support vector machine (SVM) [1, 2, 14, 29, 32, 35, 38–41], KNN [1, 2, 26, 28, 40, 41, 47, 48, 49], random forest (RF) [2, 30, 36], Bayesian network [27, 28, 40, 42, 43, 48], discrimination algorithm (DA) [27, 29, 31, 37], probabilistic neural network (PNN) [27, 43] or decision tree [31, 40, 42, 44–46].…”

Section: Introductionmentioning

confidence: 99%

Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples

et al. 2016

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BackgroundThe use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined.MethodsIn this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation.ResultsExperimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms.ConclusionsThis study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

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A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders

Hegde¹,

Shetty²,

Rai³

et al. 2019

Journal of Voice

129

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Processing and Analysis of Human Voice for Assessment of Parkinson Disease

Cited by 8 publications

References 7 publications

A multimodal screening system for elderly neurological diseases based on deep learning

A multimodal screening system for elderly neurological diseases based on deep learning

Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples

A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders

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