Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions
Parkinson’s disease (PD) is a multi-symptom neurodegenerative disease. There are no biomarkers; the diagnosis and monitoring of the disease progression require clinical and functional symptom observation. Voice impairment is an early symptom of PD, and computerized analysis of voice has b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9887934/ |
| _version_ | 1798000643888119808 |
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| author | Mohammod Abdul Motin Nemuel Daniel Pah Sanjay Raghav Dinesh Kant Kumar |
| author_facet | Mohammod Abdul Motin Nemuel Daniel Pah Sanjay Raghav Dinesh Kant Kumar |
| author_sort | Mohammod Abdul Motin |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a multi-symptom neurodegenerative disease. There are no biomarkers; the diagnosis and monitoring of the disease progression require clinical and functional symptom observation. Voice impairment is an early symptom of PD, and computerized analysis of voice has been proposed for early detection and monitoring of the disease. However, there is poor reproducibility of many studies, which is attributed to the experimental data having been collected under controlled conditions. To overcome the limitations of earlier works, this study has investigated three sustained phonemes: /a/, /o/, and /m/, which were recorded using an iOS-based smartphone from 72 participants (36 people with PD and 36 healthy) in a typical clinical setting. A number of signal features were obtained, statistically investigated, and ranked to identify the suitable feature sets. These were classified using machine learning models. The results show that a combination of phonemes /a/+/o/+/m/ was most suited to differentiate the voice of PD people from healthy control participants, with an average accuracy, sensitivity, and specificity of 100%, 100%, 100%, respectively, using leave-one-out validation. The findings of this study could assist in the clinical assessments and remote telehealth monitoring for people with parkinsonian dysarthria using smartphones. |
| first_indexed | 2024-04-11T11:23:36Z |
| format | Article |
| id | doaj.art-4994b8fdf3544265af3d99438bb12fb0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:23:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4994b8fdf3544265af3d99438bb12fb02022-12-22T04:26:27ZengIEEEIEEE Access2169-35362022-01-0110976009760910.1109/ACCESS.2022.32039739887934Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World ConditionsMohammod Abdul Motin0https://orcid.org/0000-0003-1618-3772Nemuel Daniel Pah1https://orcid.org/0000-0002-0181-3199Sanjay Raghav2Dinesh Kant Kumar3https://orcid.org/0000-0003-3602-4023Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, BangladeshSchool of Engineering, RMIT University, Melbourne, VIC, AustraliaSchool of Engineering, RMIT University, Melbourne, VIC, AustraliaSchool of Engineering, RMIT University, Melbourne, VIC, AustraliaParkinson’s disease (PD) is a multi-symptom neurodegenerative disease. There are no biomarkers; the diagnosis and monitoring of the disease progression require clinical and functional symptom observation. Voice impairment is an early symptom of PD, and computerized analysis of voice has been proposed for early detection and monitoring of the disease. However, there is poor reproducibility of many studies, which is attributed to the experimental data having been collected under controlled conditions. To overcome the limitations of earlier works, this study has investigated three sustained phonemes: /a/, /o/, and /m/, which were recorded using an iOS-based smartphone from 72 participants (36 people with PD and 36 healthy) in a typical clinical setting. A number of signal features were obtained, statistically investigated, and ranked to identify the suitable feature sets. These were classified using machine learning models. The results show that a combination of phonemes /a/+/o/+/m/ was most suited to differentiate the voice of PD people from healthy control participants, with an average accuracy, sensitivity, and specificity of 100%, 100%, 100%, respectively, using leave-one-out validation. The findings of this study could assist in the clinical assessments and remote telehealth monitoring for people with parkinsonian dysarthria using smartphones.https://ieeexplore.ieee.org/document/9887934/DysarthriaParkinson’s diseasesmartphonesustained phonemesvoice impairment |
| spellingShingle | Mohammod Abdul Motin Nemuel Daniel Pah Sanjay Raghav Dinesh Kant Kumar Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions IEEE Access Dysarthria Parkinson’s disease smartphone sustained phonemes voice impairment |
| title | Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions |
| title_full | Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions |
| title_fullStr | Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions |
| title_full_unstemmed | Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions |
| title_short | Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions |
| title_sort | parkinson x2019 s disease detection using smartphone recorded phonemes in real world conditions |
| topic | Dysarthria Parkinson’s disease smartphone sustained phonemes voice impairment |
| url | https://ieeexplore.ieee.org/document/9887934/ |
| work_keys_str_mv | AT mohammodabdulmotin parkinsonx2019sdiseasedetectionusingsmartphonerecordedphonemesinrealworldconditions AT nemueldanielpah parkinsonx2019sdiseasedetectionusingsmartphonerecordedphonemesinrealworldconditions AT sanjayraghav parkinsonx2019sdiseasedetectionusingsmartphonerecordedphonemesinrealworldconditions AT dineshkantkumar parkinsonx2019sdiseasedetectionusingsmartphonerecordedphonemesinrealworldconditions |