Analysis of EEG-Based Stroke Severity Groups Clustering using K-Means
| Autor: | MY Teguh Sulistyono, Adhi Dharma Wibawa, Evi Septiana Pane, Mauridhi Hery Purnomo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Rehabilitation medicine.diagnostic_test business.industry Brain activity and meditation medicine.medical_treatment k-means clustering Alpha (ethology) macromolecular substances Electroencephalography medicine.disease Physical medicine and rehabilitation Medicine Analysis of variance business Cluster analysis Stroke |
| Zdroj: | 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA). |
| DOI: | 10.1109/isitia52817.2021.9502250 |
| Popis: | Rehabilitation is the essential key to restore motoric function and brain activity for stroke patients. Electroencephalograph (EEG) has been used widely as an alternative tool to monitor the progress of stroke rehabilitation because EEG represents the motoric function during motion. Determining the stroke severity level is also important during rehabilitation program because it gives information to the clinicians before performing rehabilitation. Stroke severity level will determine which rehabilitation programs the patient should take. Therefore, this study aims to classify stroke severity level by using the EEG features which are the Relative Power Ratio Power Spectral Density (RPR-PSD) and Relative Power Ratio Power Percentage (RPR-PP). The data is collected through the collaboration process with Airlangga University Hospital Surabaya (RSUA). The classes of stroke severity level are defined as severe, moderate, and mild. The EEG frequency sub-bands that were analyzed are Alpha Low (8-9 Hz), Alpha High (9-13 Hz), Beta Low (13-17), and Beta High (17-30 Hz). K-Means clustering method is applied to classify the severity level. From the ANOVA significane value, it shows that all groups of severity level from all sub-bands in this study showed p-value |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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