Optimization of Sleep Apnea Detection using SpO2 and ANN

被引：0

作者：

Mostafa, Sheikh Shanawaz ^{[1
,3
]}

Carvalho, Joao Paulo ^{[2
]}

Morgado-Dias, Fernando ^{[3
,4
]}

Ravelo-Garcia, Antonio ^{[5
]}

机构：

[1] Univ Lisbon, Inst Super Tecn, Lisbon, Portugal

[2] Univ Lisbon, Inst Super Tecn, INESC ID, Lisbon, Portugal

[3] Minist Int Trade & Ind, Funchal, Portugal

[4] Univ Madeira, Funchal, Portugal

[5] Univ Los Palmas Gran Canaria, Las Palmas Gran Canaria, Spain

来源：

2017 XXVI INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND AUTOMATION TECHNOLOGIES (ICAT) | 2017年

关键词：

Classification; Feature Section; Sleep Apnea; SpO2; ELECTROCARDIOGRAM; PHYSIONET;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Repetitive respiratory disturbance during sleep is called Sleep Apnea Hypopnea Syndrome and causes various diseases. Different features and classifiers have been used by different researchers to detect sleep apnea. This study is undertaken to identify the better performing blood oxygen saturation features subset using an Artificial Neural Network classifier for sleep Apnea detection. A database of 8 subjects with one-minute annotation is used to test the proposed system. The optimized system has seven features chosen from a total set of sixty-one features presenting a high accuracy rate using a genetic algorithm. Artificial Neural Network was able to achieve 97.7 percentage of accuracy with only seven features chosen by the Genetic algorithm.

引用

页数：6

共 50 条

[21] Performance of a Commercial Smart Watch Compared to Polysomnography for SpO2 Measurement and Sleep Apnea Evaluation
Browne, S.
Vaida, F.
Deyoung, P. N.
Owens, R.
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2023, 207
[22] An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability
Li Haoyu
Li Jianxing
Arunkumar, N.
Hussein, Ahmed Faeq
Jaber, Mustafa Musa
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 98 : 69 - 77
[23] Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals
Mortazavi, Erfan
Tarvirdizadeh, Bahram
Alipour, Khalil
Ghamari, Mohammad
SCIENTIFIC REPORTS, 2024, 14 (01):
[24] A multimodal approach for sleep apnea detection: SpO2 and force sensitive sensors in a flexible 3D-printed wearable
Tiwari, Ayush
Baghel, Manoj Kumar
Kumar, Vivek
MICROCHIMICA ACTA, 2025, 192 (03)
[25] Using SpO2: Not as Simple as It Seems
Hess, Dean R.
RESPIRATORY CARE, 2023, 68 (05) : 708 - 711
[26] Contactless SpO2 Detection from Face Using Consumer Camera
Zhu, Li
Vatanparvar, Korosh
Gwak, Migyeong
Kuang, Jilong
Gao, Alex
2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI'22) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22), 2022,
[27] Performance Improvement in Obstructive Sleep Apnea Severity Classification Using Multiple Receptive Fields Feature Extraction on SpO2 Signals
Yeh, Cheng-Yu
Chi, Hung-Ying
Lin, Chun-Cheng
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2024,
[28] Sleep apnea syndrome classification based om temporal ECG and SPO2 by using multimodal multichannel transfer module with squeeze and excitation
Jiang, Mingfeng
Lou, Lijun
Zhang, Wei
Yang, Xiaocheng
Wang, Zhefeng
Wu, Yongquan
Ke, Wei
Xia, Ling
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2025, 104
[29] Risk-prediction model for incident hypertension in patients with obstructive sleep apnea based on SpO2 signals
You, Jingyuan
Li, Juan
Li, Xiaoyu
Li, Haojie
Tu, Jinying
Zhang, Yuhuan
Gao, Jiandong
Wu, Ji
Ye, Jingying
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, 2023,
[30] Effect of Decision Boundary for Logistic Regression Classifiers on Sleep Apnea Screening Accuracy with Wearable SpO2 Data
Liang, Zilu
2023 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU, 2023,

← 1 2 3 4 5 →