Developing Fast and Accurate Arrhythmia Multi-label Detection Algorithm for Real-world ECG Monitoring

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dc.contributor.advisorBraverman, Vladimiren_US
dc.contributor.committeeMemberSilva, Arleien_US
dc.creatorZheng, Thomasen_US
dc.date.accessioned2025-01-17T17:17:20Zen_US
dc.date.available2025-01-17T17:17:20Zen_US
dc.date.created2024-12en_US
dc.date.issued2024-11-21en_US
dc.date.submittedDecember 2024en_US
dc.date.updated2025-01-17T17:17:20Zen_US
dc.description.abstractArrhythmia detection is challenging due to the imbalance between normal and arrhythmia heartbeats, compounded by environmental noise in wearable devices compared to clinical settings. We propose a novel hierarchical model using CNN+BiLSTM with Attention for arrhythmia detection, featuring a binary classification for normal vs. arrhythmia beats and a multi-label classification for various arrhythmia types. We evaluated our model against several baselines on a proprietary dataset. Our model achieved 95% accuracy, 0.838 F1-score, and 0.906 AUC for binary classification, and 88% accuracy, 0.736 F1-score, and 0.875 AUC for multi-label classification, outperforming existing methods.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://hdl.handle.net/1911/118219en_US
dc.language.isoenen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectElectrocardiogramen_US
dc.subjectWearable deviceen_US
dc.subjectMulti-label classificationen_US
dc.titleDeveloping Fast and Accurate Arrhythmia Multi-label Detection Algorithm for Real-world ECG Monitoringen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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