Diagnostic tools for rapid eye movement sleep behaviour disorder
<p>There is clear evidence to support Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) as an early predictor of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. Those diagnosed with RBD enact their dreams and therefore present an abnormal characteristic of mo...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: |
| _version_ | 1797111118018117632 |
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| author | Cooray, N |
| author2 | De Vos, M |
| author_facet | De Vos, M Cooray, N |
| author_sort | Cooray, N |
| collection | OXFORD |
| description | <p>There is clear evidence to support Rapid-Eye-Movement (REM) sleep behaviour
disorder (RBD) as an early predictor of Parkinson’s disease, dementia with Lewy
bodies, and multiple system atrophy. Those diagnosed with RBD enact their
dreams and therefore present an abnormal characteristic of movement during REM sleep. This unusual sleep disorder presents an opportunity to better understand neurological degeneration and in-turn develop preventative medicine. However, manual polysomnograph (PSG) analysis is mandatory when diagnosing RBD, which demands complex monitoring equipment, specially trained staff, bed availability, and clinicians to interpret abundant amounts of data. Inevitably this process is time consuming and expensive, creating a demand for automated diagnostic support tools to better utilise the time of clinicians.</p>
<p>The first aim of this thesis was to achieve automated sleep stage classification
using signal processing and machine learning techniques. A total of 159 features were calculated using Electroencephalography (EEG), Electrooculography (EOG), and Electromyography (EMG) signals. After extracting the features a Random Forest (RF) algorithm was used to classify five sleep stages. With respect to REM stage classification, this technique achieved an accuracy of 93%, sensitivity of 63%, and specificity of 97% (five stage Cohen kappa of 0.63).</p>
<p>After sleep staging, clinicians must manually identify abnormal REM sleep
behaviour. Several methods have been proposed for RBD detection that use
EMG recordings to objectively quantify abnormal REM movement. In this work we further develop these proven techniques with additional features that incorporate the relationship of muscle movement between sleep stages and general sleep architecture. Using an RF classifier with established and additional features, the performance of RBD detection was shown to improve upon established metrics (achieving 94% accuracy, 94% sensitivity, and 94% specificity). This technique used in combination with automated sleep staging sustained a high performance in RBD detection, with an accuracy, sensitivity, and specificity of 90%, 86%, and 94%, respectively.</p>
<p>Lastly, this thesis investigated a minimal set of sensors to fully-automate the
detection of RBD, integrating automated sleep staging followed by RBD detection without the need for cumbersome EEG sensors. This thesis demonstrated a costeffective, practical, and simple RBD identification support tool using an EOG and EMG sensor to achieve an accuracy, sensitivity, and specificity of 86%, 82%, and 90%, respectively.</p> |
| first_indexed | 2024-03-07T08:04:18Z |
| format | Thesis |
| id | oxford-uuid:e0151bdc-a804-4b83-9f1f-935b6b95d289 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:04:18Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | oxford-uuid:e0151bdc-a804-4b83-9f1f-935b6b95d2892023-10-20T07:30:15ZDiagnostic tools for rapid eye movement sleep behaviour disorderThesishttp://purl.org/coar/resource_type/c_db06uuid:e0151bdc-a804-4b83-9f1f-935b6b95d289Biomedical engineeringMachine learningSleep disordersEnglishHyrax Deposit2020Cooray, NDe Vos, M<p>There is clear evidence to support Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) as an early predictor of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. Those diagnosed with RBD enact their dreams and therefore present an abnormal characteristic of movement during REM sleep. This unusual sleep disorder presents an opportunity to better understand neurological degeneration and in-turn develop preventative medicine. However, manual polysomnograph (PSG) analysis is mandatory when diagnosing RBD, which demands complex monitoring equipment, specially trained staff, bed availability, and clinicians to interpret abundant amounts of data. Inevitably this process is time consuming and expensive, creating a demand for automated diagnostic support tools to better utilise the time of clinicians.</p> <p>The first aim of this thesis was to achieve automated sleep stage classification using signal processing and machine learning techniques. A total of 159 features were calculated using Electroencephalography (EEG), Electrooculography (EOG), and Electromyography (EMG) signals. After extracting the features a Random Forest (RF) algorithm was used to classify five sleep stages. With respect to REM stage classification, this technique achieved an accuracy of 93%, sensitivity of 63%, and specificity of 97% (five stage Cohen kappa of 0.63).</p> <p>After sleep staging, clinicians must manually identify abnormal REM sleep behaviour. Several methods have been proposed for RBD detection that use EMG recordings to objectively quantify abnormal REM movement. In this work we further develop these proven techniques with additional features that incorporate the relationship of muscle movement between sleep stages and general sleep architecture. Using an RF classifier with established and additional features, the performance of RBD detection was shown to improve upon established metrics (achieving 94% accuracy, 94% sensitivity, and 94% specificity). This technique used in combination with automated sleep staging sustained a high performance in RBD detection, with an accuracy, sensitivity, and specificity of 90%, 86%, and 94%, respectively.</p> <p>Lastly, this thesis investigated a minimal set of sensors to fully-automate the detection of RBD, integrating automated sleep staging followed by RBD detection without the need for cumbersome EEG sensors. This thesis demonstrated a costeffective, practical, and simple RBD identification support tool using an EOG and EMG sensor to achieve an accuracy, sensitivity, and specificity of 86%, 82%, and 90%, respectively.</p> |
| spellingShingle | Biomedical engineering Machine learning Sleep disorders Cooray, N Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title | Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title_full | Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title_fullStr | Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title_full_unstemmed | Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title_short | Diagnostic tools for rapid eye movement sleep behaviour disorder |
| title_sort | diagnostic tools for rapid eye movement sleep behaviour disorder |
| topic | Biomedical engineering Machine learning Sleep disorders |
| work_keys_str_mv | AT coorayn diagnostictoolsforrapideyemovementsleepbehaviourdisorder |