Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea

Emre Turan; Orhan Er

Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea

Abstract

This study proposes a novel approach for the diagnosis of sleep apnea using deep learning analysis of Welch Pediograms derived from electrocardiogram (ECG) signals. Despite affecting between 3% and 17% of the general adult population, depending on diagnostic criteria used, sleep apnea remains underdiagnosed due to limitations in traditional diagnostic methods, particularly polysomnography, which is costly, requires specialized facilities, and may disrupt natural sleep patterns. Our research addresses these challenges by developing an automated diagnostic system that analyzes frequency spectrum variations in RR intervals through Welch Pediograms. These visualizations, derived from Holter monitor recordings, effectively capture the characteristic frequency-domain changes in heart rate variability associated with sleep apnea episodes. Using Convolutional Neural Networks (CNNs), our methodology processes these spectral representations to identify patterns indicative of sleep apnea. This approach offers significant advantages including non-invasive monitoring, home-based testing capability, and improved patient comfort. Our findings contribute to both the methodological development in sleep medicine and the practical implementation of accessible diagnostic tools for sleep apnea, potentially improving early detection rates and patient outcomes.

Keywords

Supporting Institution

İzmir Bakırçay University Artificial Intelligence in Health Application and Research Centre

Ethical Statement

There is no ethical violation.

Thanks

This study was developed under the roof of Izmir Bakırçay University Artificial Intelligence Studies in Health Application and Research Centre and Amatis Information Technologies.

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Details

Primary Language

English

Subjects

Deep Learning, Health Informatics and Information Systems

Journal Section

Research Article

Authors

Emre Turan ^*
0009-0003-5631-8675
Türkiye

Orhan Er
0000-0002-4732-9490
Türkiye

Publication Date

October 1, 2025

Submission Date

April 10, 2025

Acceptance Date

September 10, 2025

Published in Issue

Year 2025 Volume: 5 Number: 2

IZ

https://izlik.org/JA54SF32PJ

Cite

RIS / Bibtex

APA

Turan, E., & Er, O. (2025). Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea. Artificial Intelligence Theory and Applications, 5(2), 33-48. https://izlik.org/JA54SF32PJ

AMA

1.Turan E, Er O. Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea. AITA. 2025;5(2):33-48. https://izlik.org/JA54SF32PJ

Chicago

Turan, Emre, and Orhan Er. 2025. “Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea”. Artificial Intelligence Theory and Applications 5 (2): 33-48. https://izlik.org/JA54SF32PJ.

EndNote

Turan E, Er O (October 1, 2025) Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea. Artificial Intelligence Theory and Applications 5 2 33–48.

IEEE

[1]E. Turan and O. Er, “Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea”, AITA, vol. 5, no. 2, pp. 33–48, Oct. 2025, [Online]. Available: https://izlik.org/JA54SF32PJ

ISNAD

Turan, Emre - Er, Orhan. “Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea”. Artificial Intelligence Theory and Applications 5/2 (October 1, 2025): 33-48. https://izlik.org/JA54SF32PJ.

JAMA

1.Turan E, Er O. Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea. AITA. 2025;5:33–48.

MLA

Turan, Emre, and Orhan Er. “Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea”. Artificial Intelligence Theory and Applications, vol. 5, no. 2, Oct. 2025, pp. 33-48, https://izlik.org/JA54SF32PJ.

Vancouver

1.Emre Turan, Orhan Er. Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea. AITA [Internet]. 2025 Oct. 1;5(2):33-48. Available from: https://izlik.org/JA54SF32PJ