Compressor-Based Classification for Atrial Fibrillation Detection
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Compressor-Based Classification for Atrial Fibrillation Detection
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| Автор |
Markov, N.
Ushenin, K. Bozhko, Y. Solovyova, O. |
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| Тематика |
ATRIAL FIBRILLATION
ECG GZIP NORMALIZED COMPRESSION DISTANCE BIOMEDICAL ENGINEERING CLASSIFICATION (OF INFORMATION) COMPRESSORS DISEASES NEAREST NEIGHBOR SEARCH STOCHASTIC SYSTEMS TEXT PROCESSING ATRIAL FIBRILLATION AUTOMATIC DETECTION BINARY CLASSIFICATION GZIP HEALTH IMPLICATIONS INTERVAL SEQUENCES K-NEAREST NEIGHBORS CLASSIFIERS NORMALIZED COMPRESSION DISTANCE RR INTERVALS TEXT CLASSIFICATION ELECTROCARDIOGRAMS |
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| Описание |
Atrial fibrillation (AF) is one of the most common arrhythmias with challenging public health implications. Therefore, automatic detection of AF episodes on ECG is one of the essential tasks in biomedical engineering. In this paper, we applied the recently introduced method of compressor-based text classification with gzip algorithm for AF detection (binary classification between heart rhythms). We investigated the normalized compression distance applied to RR-interval and ΔRR-interval sequences (ΔRR-interval is the difference between subsequent RR-intervals). Here, the configuration of the k-nearest neighbour classifier, an optimal window length, and the choice of data types for compression were analyzed. We achieved good classification results while learning on the full MIT-BIH Atrial Fibrillation database, close to the best specialized AF detection algorithms (avg. sensitivity = 97.1%, avg. specificity = 91.7%, best sensitivity of 99.8%, best specificity of 97.6% with fivefold cross-validation). In addition, we evaluated the classification performance under the few-shot learning setting. Our results suggest that gzip compression-based classification, originally proposed for texts, is suitable for biomedical data and quantized continuous stochastic sequences in general. © 2023 IEEE.
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| Дата |
2024-04-05T16:38:11Z
2024-04-05T16:38:11Z 2023 |
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| Тип |
Conference Paper
Conference object (info:eu-repo/semantics/conferenceObject) info:eu-repo/semantics/submittedVersion |
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| Идентификатор |
Markov, N, Ushenin, K, Bozhko, Y & Solovyova, O 2023, Compressor-Based Classification for Atrial Fibrillation Detection. в 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc., стр. 122-127, 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), 28/09/2023. https://doi.org/10.1109/CSGB60362.2023.10329826
Markov, N., Ushenin, K., Bozhko, Y., & Solovyova, O. (2023). Compressor-Based Classification for Atrial Fibrillation Detection. в 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings: book (стр. 122-127). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB60362.2023.10329826 9798350307979 Final All Open Access, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180368595&doi=10.1109%2fCSGB60362.2023.10329826&partnerID=40&md5=3ce4376ca764fa9c668a6c076fadf6ad https://arxiv.org/pdf/2308.13328 http://elar.urfu.ru/handle/10995/131072 10.1109/CSGB60362.2023.10329826 85180368595 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
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| Формат |
application/pdf
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| Издатель |
Institute of Electrical and Electronics Engineers Inc.
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| Источник |
2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)
2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings |
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