Atrial fibrillation episodes detection based on classification of heart rate derived features
| Autor: | Norbert Henzel, Janusz Wrobel, Krzysztof Horoba |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
education.field_of_study medicine.diagnostic_test business.industry 0206 medical engineering Population Cardiac arrhythmia Beat (acoustics) Atrial fibrillation Linear classifier 02 engineering and technology 030204 cardiovascular system & hematology medicine.disease 020601 biomedical engineering 03 medical and health sciences 0302 clinical medicine Internal medicine Heart rate cardiovascular system Cardiology medicine education Hidden Markov model business Electrocardiography |
| Zdroj: | MIXDES |
| DOI: | 10.23919/mixdes.2017.8005278 |
| Popis: | Atrial fibrillation (AF) is one of the most common cardiac arrhythmia and effects nearly 1–2 of every 100 persons of the population. This paper evaluates the effectiveness of Machine Learning (ML) approach to detect AF episodes. Features, determined exclusively on the basis of beat intervals, are classified with linear classifier. Performances of the proposed approach are evaluated by means of the MIT-BIH Atrial Fibrillation Database. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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