Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms
Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023020315 |
| _version_ | 1797837015159406592 |
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| author | Minja Belić Zaharije Radivojević Vladislava Bobić Vladimir Kostić Milica Đurić-Jovičić |
| author_facet | Minja Belić Zaharije Radivojević Vladislava Bobić Vladimir Kostić Milica Đurić-Jovičić |
| author_sort | Minja Belić |
| collection | DOAJ |
| description | Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research. |
| first_indexed | 2024-04-09T15:19:08Z |
| format | Article |
| id | doaj.art-4a30748d5c6349dbac4c177dc52c9c18 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T15:19:08Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-4a30748d5c6349dbac4c177dc52c9c182023-04-29T14:51:11ZengElsevierHeliyon2405-84402023-04-0194e14824Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonismsMinja Belić0Zaharije Radivojević1Vladislava Bobić2Vladimir Kostić3Milica Đurić-Jovičić4University of Belgrade, Serbia; Corresponding author.School of Electrical Engineering, University of Belgrade, SerbiaInnovation Center of the School of Electrical Engineering in Belgrade, SerbiaSerbian Academy of Sciences and Arts, Belgrade, SerbiaInnovation Center of the School of Electrical Engineering in Belgrade, SerbiaBackground: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.http://www.sciencedirect.com/science/article/pii/S2405844023020315Parkinson's diseaseAtypical parkinsonismsDiagnosticsMachine learningInertial sensorsFinger tapping |
| spellingShingle | Minja Belić Zaharije Radivojević Vladislava Bobić Vladimir Kostić Milica Đurić-Jovičić Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms Heliyon Parkinson's disease Atypical parkinsonisms Diagnostics Machine learning Inertial sensors Finger tapping |
| title | Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms |
| title_full | Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms |
| title_fullStr | Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms |
| title_full_unstemmed | Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms |
| title_short | Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms |
| title_sort | quick computer aided differential diagnostics based on repetitive finger tapping in parkinson s disease and atypical parkinsonisms |
| topic | Parkinson's disease Atypical parkinsonisms Diagnostics Machine learning Inertial sensors Finger tapping |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023020315 |
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