Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks
The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtai...
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10323348/ |
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| author | Bipasha Kashyap Pubudu N. Pathirana Malcolm Horne Laura Power David J. Szmulewicz |
| author_facet | Bipasha Kashyap Pubudu N. Pathirana Malcolm Horne Laura Power David J. Szmulewicz |
| author_sort | Bipasha Kashyap |
| collection | DOAJ |
| description | The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman’s rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework’s usefulness for identifying and monitoring Ataxic Speech. |
| format | Article |
| id | doaj-art-2a91b7da244744b6a7f64fd9b136a3eb |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-2a91b7da244744b6a7f64fd9b136a3eb2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01314839485010.1109/TNSRE.2023.333471810323348Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech TasksBipasha Kashyap0https://orcid.org/0000-0002-9469-858XPubudu N. Pathirana1https://orcid.org/0000-0001-8014-7798Malcolm Horne2https://orcid.org/0000-0001-9427-2100Laura Power3David J. Szmulewicz4https://orcid.org/0000-0002-7013-9329Networked and Sensing Control (NSC) Laboratory, School of Engineering, Deakin University, Waurn Ponds, VIC, AustraliaFlorey Institute of Neuroscience and Mental Health, Parkville, VIC, AustraliaFlorey Institute of Neuroscience and Mental Health, Parkville, VIC, AustraliaBalance Disorders & Ataxia Service, Royal Victorian Eye and Ear Hospital (RVEEH), East Melbourne, VIC, AustraliaFlorey Institute of Neuroscience and Mental Health, Parkville, VIC, AustraliaThe assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman’s rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework’s usefulness for identifying and monitoring Ataxic Speech.https://ieeexplore.ieee.org/document/10323348/Cerebellar ataxiaspeech processingcerebellar dysarthria |
| spellingShingle | Bipasha Kashyap Pubudu N. Pathirana Malcolm Horne Laura Power David J. Szmulewicz Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks IEEE Transactions on Neural Systems and Rehabilitation Engineering Cerebellar ataxia speech processing cerebellar dysarthria |
| title | Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks |
| title_full | Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks |
| title_fullStr | Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks |
| title_full_unstemmed | Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks |
| title_short | Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks |
| title_sort | machine learning based scoring system to predict the risk and severity of ataxic speech using different speech tasks |
| topic | Cerebellar ataxia speech processing cerebellar dysarthria |
| url | https://ieeexplore.ieee.org/document/10323348/ |
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