Sleep Apnea Detection From Raw Ecg Signal Using Deep Learning And Machine Learning

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dc.contributor.advisorMd. Shahriar Hussain
dc.contributor.authorSalem Shamsul Alam
dc.contributor.authorSumit Saha
dc.contributor.id1931849642
dc.contributor.id1931415042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-01
dc.date.accessioned2025-07-01T04:50:43Z
dc.date.available2025-07-01T04:50:43Z
dc.date.issued2023
dc.description.abstractSleep apnea, a prevalent yet underdiagnosed sleep disorder, necessitates robust and accurate diagnostic tools. In this project, we undertook an in-depth exploration of machine learning (ML) and deep learning (DL) models for sleep apnea detection, specifically utilizing raw electrocardiogram (ECG) signals. Our comparative analysis encompassed a range of ML models, including Random Forest, Logistic Regression, Decision Tree, AdaBoost, and XGBoost, and a specialized 1D-CNN model within the DL domain. Results underscore the exceptional performance of the 1D-CNN model, achieving a remarkable accuracy of 99.56%, sensitivity of 96.05%, and specificity of 99.66%. This outperforms traditional ML models, signifying the prowess of DL in extracting intricate patterns from raw ECG signals for accurate sleep apnea detection. The 1D-CNN model's ability to discern subtle features proves crucial for accurately identifying apnea events. Our study not only emphasizes the effectiveness of the 1D-CNN model for sleep apnea detection and highlights the transformative potential of deep learning in healthcare diagnostics. This research contributes valuable insights into the optimal choice of models for sleep apnea detection, paving the way for enhanced diagnostic accuracy and improved patient care.
dc.description.degreeUndergraduate
dc.identifier.cd600000603
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1202
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleSleep Apnea Detection From Raw Ecg Signal Using Deep Learning And Machine Learning
dc.typeThesis
oaire.citation.endPage44
oaire.citation.startPage1
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