eprintid: 9684
rev_number: 6
eprint_status: archive
userid: 46
dir: disk0/00/00/96/84
datestamp: 2025-06-21 01:59:43
lastmod: 2025-06-21 01:59:43
status_changed: 2025-06-21 01:59:43
type: thesis
metadata_visibility: show
creators_name: Fadli R., Muhammad Taufiq
creators_NPM: 065118165
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Chairunnas, Andi
contributors_name: Suriansyah, Muhamad Iqbal
corp_creators: Universitas Pakuan
corp_creators: Fakultas Matematika dan Ilmu Pnegetahuan Alam
corp_creators: Program Studi Ilmu Komputer
title: Sistem Diteksi Insomnia Basis Metode Naive Bayes Menggunakan Sensor GSR dan Max30102
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
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
date: 2024-07-18
date_type: published
institution: Universitas Pakuan
department: Fakultas Matematika dan Pengetahuan Alam
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Fadli R., Muhammad Taufiq  (2024) Sistem Diteksi Insomnia Basis Metode Naive Bayes Menggunakan Sensor GSR dan Max30102.  Skripsi thesis, Universitas Pakuan.