Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors
Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the main clinical manifestations o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/10/4/214 |
| _version_ | 1797570858730913792 |
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| author | Yulia Shichkina Elizaveta Stanevich Yulia Irishina |
| author_facet | Yulia Shichkina Elizaveta Stanevich Yulia Irishina |
| author_sort | Yulia Shichkina |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone’s sensors) and interactive (data directly entered by the user). For the classification of the patient’s condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability. |
| first_indexed | 2024-03-10T20:31:26Z |
| format | Article |
| id | doaj.art-d74e3482d3d34f9bb264a075828d9c25 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T20:31:26Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-d74e3482d3d34f9bb264a075828d9c252023-11-19T21:26:05ZengMDPI AGDiagnostics2075-44182020-04-0110421410.3390/diagnostics10040214Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone SensorsYulia Shichkina0Elizaveta Stanevich1Yulia Irishina2St. Petersburg State Electrotechnical University “LETI”, 197376 St. Petersburg, RussiaSt. Petersburg State Electrotechnical University “LETI”, 197376 St. Petersburg, RussiaN.P. Bechtereva Institute of the Human Brain of the Russian Academy of Sciences, 197376 St. Petersburg, RussiaParkinson’s disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient’s condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone’s sensors) and interactive (data directly entered by the user). For the classification of the patient’s condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.https://www.mdpi.com/2075-4418/10/4/214Parkinson’s diseaserecurrent neural networksmartphonemotion sensormonitoring the condition of patients |
| spellingShingle | Yulia Shichkina Elizaveta Stanevich Yulia Irishina Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors Diagnostics Parkinson’s disease recurrent neural network smartphone motion sensor monitoring the condition of patients |
| title | Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors |
| title_full | Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors |
| title_fullStr | Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors |
| title_full_unstemmed | Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors |
| title_short | Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors |
| title_sort | assessment of the status of patients with parkinson s disease using neural networks and mobile phone sensors |
| topic | Parkinson’s disease recurrent neural network smartphone motion sensor monitoring the condition of patients |
| url | https://www.mdpi.com/2075-4418/10/4/214 |
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