Fadli R., Muhammad Taufiq (2024) Sistem Diteksi Insomnia Basis Metode Naive Bayes Menggunakan Sensor GSR dan Max30102. Skripsi thesis, Universitas Pakuan.
Full text not available from this repository.Abstract
Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network Erdenebayar Urtnasan1 & Jong-Uk Park1 & Eun-Yeon Joo2 & Kyoung-Joung Lee1 Received: 28 February 2018 /Accepted: 16 April 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA. Keywords Obstructive sleep apnea . Single-lead ECG . Convolutional neural network
| Item Type: | Thesis (Skripsi) |
|---|---|
| Subjects: | Fakultas Ilmu Pengetahuan Alam dan Matematika > Ilmu Komputer |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Ilmu Komputer |
| Depositing User: | PERPUSTAKAAN FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNPAK |
| Date Deposited: | 21 Jun 2025 01:59 |
| Last Modified: | 21 Jun 2025 01:59 |
| URI: | http://eprints.unpak.ac.id/id/eprint/9684 |
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