OBSTRÜKTİF UYKU APNESİ TESPİTİNDE POLİSOMNOGRAFİYE ALTERNATİF YENİ YÖNTEMLER

İsrafil Karadöl

doi:10.17780/ksujes.1205807

EN TR

NEW ALTERNATİVE METHODS TO POLYSOMNOGRAPHY İN THE DETECTİON OF OBSTRUCTİVE SLEEP APNEA

Abstract

In recent years, it is estimated that obstructive sleep apnea has become widespread due to excess weight and obesity due to dietary habits. As a result of not detecting this widespread disease, stroke, diabetes, cardiovascular disorder, nervous system diseases and work accidents due to insomnia are observed. The gold standard method used in the diagnosis of obstructive sleep apnea; are polysomnography tests performed in sleep clinics. In the polysomnography test, the person is kept in the hospital for one night and their physiological signals are monitored. But this process is costly and not accessible to the general public. The aim of this study is to investigate the new methods developed as an alternative to the polysomnography test and to evaluate the performance of these methods As a result of the investigation and evaluation, it has been seen that obstructive sleep apnea can be detected with one or more physiological signals. These methods have been examined in detail by classifying them as requiring or not requiring patient contact. As a result, when we evaluated the articles for the diagnosis of obstructive sleep apnea on an engineering basis, it was seen that deep learning based on machine learning came to the fore. In addition, it was concluded that methods that do not require patient contact are inadequate compared to other methods used to detect obstructive sleep apnea.

Keywords

Kaynakça

Akbarian, S., Ghahjaverestan, N. M., Yadollahi, A., & Taati, B. (2020). Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study. Journal of Medical Internet Research, 22(5), 1–14. https://doi.org/10.2196/17252
Akbarian, S., Ghahjaverestan, N. M., Yadollahi, A., & Taati, B. (2021). Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation. Journal of Medical Internet Research, 23(11), e26524. https://doi.org/10.2196/26524
Ben-Israel, N., Tarasiuk, A., & Zigel, Y. (2012). Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults. Sleep, 35(9), 1299–1305. https://doi.org/10.5665/sleep.2092
Botelho, M. C., Trancoso, I., Abad, A., & Paiva, T. (2019, May). Speech as a biomarker for obstructive sleep apnea detection. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5851-5855). IEEE.
Bozkurt, F., Uçar, M. K., Bozkurt, M. R., & Bilgin, C. (2020). Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea. Irbm, 41(5), 241–251. https://doi.org/10.1016/j.irbm.2020.05.006
Cao, K., & Lv, X. (2022). Multi-task feature fusion network for Obstructive Sleep Apnea detection using single-lead ECG signal. Measurement: Journal of the International Measurement Confederation, 202(March), 111787. https://doi.org/10.1016/j.measurement.2022.111787
Chen, X., Chen, Y., Ma, W., Fan, X., & Li, Y. (2021, December). SE-MSCNN: A Lightweight Multi-scaled Fusion Network for Sleep Apnea Detection Using Single-Lead ECG Signals. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1276-1280). IEEE.
Chen, X., Chen, Y., Ma, W., Fan, X., & Li, Y. (2022). Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowledge-Based Systems, 247. https://doi.org/10.1016/j.knosys.2022.108783

Ciolek, M., Niedzwiecki, M., Sieklicki, S., Drozdowski, J., & Siebert, J. (2015). Automated detection of sleep apnea and hypopnea events based on robust airflow envelope tracking in the presence of breathing artifacts. IEEE Journal of Biomedical and Health Informatics, 19(2), 418–429. https://doi.org/10.1109/JBHI.2014.2325997
Dafna, E., Tarasiuk, A., & Zigel, Y. (2013). Automatic detection of whole night snoring events using non-contact microphone. PLoS ONE, 8(12). https://doi.org/10.1371/journal.pone.0084139
de Almeida, F. R., Ayas, N. T., Otsuka, R., Ueda, H., Hamilton, P., Ryan, F. C., & Lowe, A. A. (2006). Nasal pressure recordings to detect obstructive sleep apnea. Sleep and Breathing, 10(2), 62–69. https://doi.org/10.1007/s11325-005-0042-x
Ding, L., Peng, J., Song, L., & Zhang, X. (2023). Biomedical Signal Processing and Control Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM. Biomedical Signal Processing and Control, 80(P2), 104351. https://doi.org/10.1016/j.bspc.2022.104351
Ding, Y., Wang, J., Gao, J., Fang, Q., Li, Y., Xu, W., Wu, J., & Han, D. (2021). Severity evaluation of obstructive sleep apnea based on speech features. Sleep and Breathing, 25(2), 787–795. https://doi.org/10.1007/s11325-020-02168-0
Dursunoǧlu, D., & Dursunoǧlu, N. (2010). Kalp yetersizliǧi ve uyku apnesi. Turk Kardiyoloji Dernegi Arsivi, 38(2), 135–143.
Flenady, T., Dwyer, T., & Applegarth, J. (2017). Accurate respiratory rates count: So should you! Australasian Emergency Nursing Journal, 20(1), 45–47. https://doi.org/10.1016/j.aenj.2016.12.003
Geçkil, A. A. (2022). Tedaviyle ortaya çıkan santral uyku apnesi ( Te-Csa ). Journal of Medical Topics & Updates, 1(1), 28–31.
Gürüler, H. (2012). Ekg işaretlerinden ysa ve korelasyon matrislerine dayali tikayici uyku apnesi teşhisi. Doktora Tezi. Sakarya Üniversitesi Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Eğitimi Ana Bilim Dalı, Sakarya 87s.
Gutierrez-Tobal, G. C., Alvarez, D., Del Campo, F., & Hornero, R. (2016). Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome from Single-Channel Airflow. IEEE Transactions on Biomedical Engineering, 63(3), 636–646. https://doi.org/10.1109/TBME.2015.2467188
Gutiérrez-Tobal, G. C., Álvarez, D., Vaquerizo-Villar, F., Crespo, A., Kheirandish-Gozal, L., Gozal, D., del Campo, F., & Hornero, R. (2021). Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults. Applied Soft Computing, 111, 107827. https://doi.org/10.1016/j.asoc.2021.107827
Haidar, R., Koprinska, I., & Jeffries, B. (2020, July). Sleep apnea event prediction using convolutional neural networks and Markov chains. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Hairston, I. S. (2017). The Use of WatchPATTM for Home Sleep Testing Assessment of Sleep-Related Disordered Breathing (SDB) in Heart Disease Patients - Clinical & Operational Benefits. In İtmar Medical.
Han, J., Shin, H. B., Jeong, D. U., & Park, K. S. (2008). Detection of apneic events from single channel nasal airflow using 2nd derivative method. Computer Methods and Programs in Biomedicine, 91(3), 199–207. https://doi.org/10.1016/j.cmpb.2008.04.012
Hou, L., Pan, Q., Yi, H., Shi, D., Shi, X., & Yin, S. (2021). Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds. Frontiers in Digital Health, 2(February), 1–8. https://doi.org/10.3389/fdgth.2020.613725
Huang, W., Guo, B., Shen, Y., & Tang, X. (2017). A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals. Computers in Biology and Medicine, 88(February), 32–40. https://doi.org/10.1016/j.compbiomed.2017.06.015
Hwang, S. H., Lee, H. J., Yoon, H. N., Jung, D. W., Lee, Y. J. G., Lee, Y. J., Jeong, D. U., & Park, K. S. (2014). Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor. IEEE Transactions on Biomedical Engineering, 61(7), 2125–2134. https://doi.org/10.1109/TBME.2014.2314452
Islam, S. M., Rahman, A., Yavari, E., Baboli, M., Boric-Lubecke, O., & Lubecke, V. M. (2020, January). Identity authentication of OSA patients using microwave Doppler radar and machine learning classifiers. In 2020 IEEE Radio and Wireless Symposium (RWS) (pp. 251-254). IEEE.
Javaid, A. Q., Noble, C. M., Rosenberg, R., & Weitnauer, M. A. (2015, December). Towards sleep apnea screening with an under-the-mattress IR-UWB radar using machine learning. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 837-842). IEEE.
Jayatilaka, G., Weligampola, H., Sritharan, S., Pathmanathan, P., Ragel, R., & Nawinne, I. (2019, December). Non-contact infant sleep apnea detection. In 2019 14th Conference on Industrial and Information Systems (ICIIS) (pp. 260-265). IEEE.
Jiménez-García, J., García, M., Gutiérrez-Tobal, G. C., Kheirandish-Gozal, L., Vaquerizo-Villar, F., Álvarez, D., Del Campo, F., Gozal, D., & Hornero, R. (2022). A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry. Computers in Biology and Medicine, 147(June), 105784. https://doi.org/10.1016/j.compbiomed.2022.105784
Jin, J., & Sanchez-Sinencio, E. (2015). A home sleep apnea screening device with time-domain signal processing and autonomous scoring capability. IEEE Transactions on Biomedical Circuits and Systems, 9(1), 96–104. https://doi.org/10.1109/TBCAS.2014.2314301
Kalkbrenner, C., Eichenlaub, M., Rüdiger, S., Kropf-Sanchen, C., Rottbauer, W., & Brucher, R. (2018). Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders. Medical and Biological Engineering and Computing, 56(4), 671–681. https://doi.org/10.1007/s11517-017-1706-y
Kang, B., Dang, X., & Wei, R. (2017, December). Snoring and apnea detection based on hybrid neural networks. In 2017 International Conference on Orange Technologies (ICOT) (pp. 57-60). IEEE.
Kapoor, M., & Greenough, G. (2015). Home sleep tests for obstructive sleep apnea (OSA). Journal of the American Board of Family Medicine, 28(4), 504–509. https://doi.org/10.3122/jabfm.2015.04.140266
Karamustafaoğlu, G., Akan, A., & Saatçi, E. Polisomnografi Sinyallerinin İşlenmesi ile Uyku Apnesinin Otomatik Teşhisi, 14. Tıp Teknolojileri Ulusal Kongreleri (pp. 295–298).
Karunajeewa, A. S., Abeyratne, U. R., & Hukins, C. (2011). Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. Physiological Measurement, 32(1), 83–97. https://doi.org/10.1088/0967-3334/32/1/006
Kim, J. W., Kim, T., Shin, J., Choe, G., Lim, H. J., Rhee, C. S., Lee, K., & Cho, S. W. (2019). Prediction of obstructive sleep apnea based on respiratory sounds recorded between sleep onset and sleep offset. Clinical and Experimental Otorhinolaryngology, 12(1), 72–78. https://doi.org/10.21053/ceo.2018.00388
Leino, A., Nikkonen, S., Kainulainen, S., Korkalainen, H., Töyräs, J., Myllymaa, S., Leppänen, T., Ylä-Herttuala, S., Westeren-Punnonen, S., Muraja-Murro, A., Jäkälä, P., Mervaala, E., & Myllymaa, K. (2021). Neural network analysis of nocturnal SpO2 signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease. Sleep Medicine, 79, 71–78. https://doi.org/10.1016/j.sleep.2020.12.032
Leppänen, T., Töyräs, J., Mervaala, E., Penzel, T., & Kulkas, A. (2017). Severity of individual obstruction events increases with age in patients with obstructive sleep apnea. Sleep Medicine, 37, 32–37. https://doi.org/10.1016/j.sleep.2017.06.004
Li, K., Pan, W., Li, Y., Jiang, Q., & Liu, G. (2018). A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing, 294, 94–101. https://doi.org/10.1016/j.neucom.2018.03.011
Lin, C. Y., Wang, Y. W., Setiawan, F., Trang, N. T. H., & Lin, C. W. (2022). Sleep apnea classification algorithm development using a machine-learning framework and bag-of-features derived from electrocardiogram spectrograms. Journal of Clinical Medicine, 11(1). https://doi.org/10.3390/jcm11010192
Lin, X., Cheng, H., Lu, Y., Luo, H., Li, H., Qian, Y., Zhou, L., Zhang, L., & Wang, M. (2022). Contactless sleep apnea detection in snoring signals using hybrid deep neural networks targeted for embedded hardware platform with real-time applications. Biomedical Signal Processing and Control, https://doi.org/10.1016/j.bspc.2022.103765
Liu, Y., Feng, Y., Li, Y., Xu, W., Wang, X., & Han, D. (2022). Automatic classification of the obstruction site in obstructive sleep apnea based on snoring sounds. American Journal of Otolaryngology - Head and Neck Medicine and Surgery, 43(6), 103584. https://doi.org/10.1016/j.amjoto.2022.103584
Lorato, I., Stuijk, S., Meftah, M., Kommers, D., Andriessen, P., van Pul, C., & de Haan, G. (2021). Automatic separation of respiratory flow from motion in thermal videos for infant apnea detection. Sensors, 21(18), 1–16. https://doi.org/10.3390/s21186306
Mack, D. C., Alwan, M., Turner, B., Suratt, P., & Felder, R. A. (2006, April). A passive and portable system for monitoring heart rate and detecting sleep apnea and arousals: Preliminary validation. In 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2. (pp. 51-54). IEEE.
Mendonça, F., Mostafa, S. S., Ravelo-García, A. G., Morgado-Dias, F., & Penzel, T. (2019). A Review of Obstructive Sleep Apnea Detection Approaches. IEEE Journal of Biomedical and Health Informatics, 23(2), 825–837. https://doi.org/10.1109/JBHI.2018.2823265
Misra, A., Rani, G., & Dhaka, V. S. (2022). Obstructive Sleep-Apnea Detection using Signal Preprocessing and 1-D Channel Attention Network. 5–10.
Moradhasel, B., Sheikhani, A., Aloosh, O., & Jafarnia Dabanloo, N. (2023). Spectrogram classification of patient chin electromyography based on deep learning: A novel method for accurate diagnosis obstructive sleep apnea. Biomedical Signal Processing and Control, 79(P2), 104215. https://doi.org/10.1016/j.bspc.2022.104215
Nakano, H., Furukawa, T., & Tanigawa, T. (2019). Tracheal sound analysis using a deep neural network to detect sleep apnea. Journal of Clinical Sleep Medicine, 15(8), 1125–1133. https://doi.org/10.5664/jcsm.7804
Nguyen, A.-T., Nguyen, T., Le, H.-K., Pham, H.-H., & Do, C. (2022, November). A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals. 2022 APSIPA Annual Summit and Conference. http://arxiv.org/abs/2208.03408
P Pang, K. G., Hsung, T. C., Law, A. K. W., & Choi, W. W. (2020, September). Optimal vowels measurements for Obstructive Sleep Apnea Detection Using Speech Signals. In 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) (pp. 143-147). IEEE.
Paul, T., Hassan, O., Alaboud, K., Islam, H., Rana, M. K. Z., Islam, S. K., & Mosa, A. S. M. (2022, December). ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network. In AMIA Annual Symposium Proceedings (pp. 379–385).
Penzel, T., & Sabil, A. K. (2017). The use of tracheal sounds for the diagnosis of sleep apnoea. Breathe, 13(2), e37–e45. https://doi.org/10.1183/20734735.008817
Pozo, R. F., Murillo, J. L. B., Gmez, L. H., Gonzalo, E. L., Ramírez, J. A., & Toledano, D. T. (2009). Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques. Eurasip Journal on Advances in Signal Processing, 2009. https://doi.org/10.1155/2009/982531
Sani, S. (2021, November). A New Approach for Identifying Patients with Obstructive Sleep Apnea Using K-Nearest Neighbor Classification. In 2021 International Conference on e-Health and Bioengineering (EHB) (pp. 1-4). IEEE.
Scebba, G., Da Poian, G., & Karlen, W. (2021). Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea. IEEE Transactions on Biomedical Engineering, 68(1), 350–359. https://doi.org/10.1109/TBME.2020.2993649
Selvaraj, N., & Narasimhan, R. (2013, July). Detection of sleep apnea on a per-second basis using respiratory signals. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2124-2127). IEEE.
Senaratna, C. V., Perret, J. L., Lodge, C. J., Lowe, A. J., Campbell, B. E., Matheson, M. C., Hamilton, G. S., & Dharmage, S. C. (2017). Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Medicine Reviews, 34, 70–81. https://doi.org/10.1016/j.smrv.2016.07.002
Senthil Kumar, P. K., Nithya, V. K., & Vimala Juliet, A. (2016). Micro system with mems sensor for detecting sleep Apnea. ARPN Journal of Engineering and Applied Sciences, 11(3), 2097–2101.
Setiawan, F., & Lin, C.-W. (2022). A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram. Life, 12(10), 1509. https://doi.org/10.3390/life12101509
Sharma, M., Agarwal, S., & Acharya, U. R. (2018). Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals. Computers in Biology and Medicine, 100(April), 100–113. https://doi.org/10.1016/j.compbiomed.2018.06.011
Sharma, M., Kumbhani, D., Tiwari, J., Kumar, T. S., & Acharya, U. R. (2022). Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals. Computers in Biology and Medicine, 144(February), 105364. https://doi.org/10.1016/j.compbiomed.2022.105364
Sharma, M., Kumbhani, D., Yadav, A., & Acharya, U. R. (2022). Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals. Applied Intelligence, 52(2), 1325–1337. https://doi.org/10.1007/s10489-021-02422-2
Shen, Q., Yang, X., Zou, L., Wei, K., Wang, C., & Liu, G. (2022). Multi-Task Multi-Attention Residual Shrinkage Convolutional Neural Network for Sleep Apnea Detection Based on Wearable Bracelet Photoplethysmography. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2022.3195777
Sheta, A., Turabieh, H., Thaher, T., Too, J., Mafarja, M., Hossain, M. S., & Surani, S. R. (2021). Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers. Applied Sciences (Switzerland), 11(14). https://doi.org/10.3390/app11146622
Shin, J. H., Chee, Y. J., Jeong, D. U., & Park, K. S. (2010). Nonconstrained sleep monitoring system and algorithms using air-mattress with balancing tube method. IEEE Transactions on Information Technology in Biomedicine, 14(1), 147–156. https://doi.org/10.1109/TITB.2009.2034011
Ruby S.M., Dafna, E., & Zigel, Y. (2018). Obstructive Sleep Apnea (OSA) classification using analysis of breathing sounds during speech. European Signal Processing Conference, 2018-Septe(1403), 1132–1136. https://doi.org/10.23919/EUSIPCO.2018.8553353
Ruby S. M., Dafna, E., & Zigel, Y. (2020). Diagnosis of Obstructive Sleep Apnea Using Speech Signals from Awake Subjects. IEEE Journal on Selected Topics in Signal Processing, 14(2), 251–260. https://doi.org/10.1109/JSTSP.2019.2955019
Sola-Soler, J., Jane, R., Fiz, J. A., & Morera, J. (2007, August). Automatic classification of subjects with and without sleep apnea through snoring analysis. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 6093-6096). IEEE.
Surrel, G., Aminifar, A., Rincón, F., Murali, S., & Atienza, D. (2018). Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors. IEEE Transactions on Biomedical Circuits and Systems, 12(4), 762–773. https://doi.org/10.1109/TBCAS.2018.2824659
Toften, S., Kjellstadli, J. T., Tyvold, S. S., & Moxness, M. H. S. (2021). A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning. Journal of Sensors, 2021. https://doi.org/10.1155/2021/2998202
Uçar, M. K. (2017). Obstrüktif Uyku Apne Teşhisi için Makine Öğrenmesi Tabanlı Yeni Bir Yöntem Geliştirilmesi. Doktora Tezi. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Elektrik-Elektronik Mühendisliği Ana Bilim Dalı, Sakarya 139s.
Vaquerizo-Villar, F., Alvarez, D., Gutierrez-Tobal, G. C., Campo, F. Del, Kheirandish-Gozal, L., Gozal, D., Penzel, T., & Hornero, R. (2022, June). A convolutional neural network to classify sleep stages in pediatric sleep apnea from pulse oximetry signals. In 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) (pp. 108-113). IEEE.
Vaquerizo-Villar, F., Alvarez, D., Kheirandish-Gozal, L., Gutierrez-Tobal, G. C., Barroso-Garcia, V., Santamaria-Vazquez, E., Campo, F. Del, Gozal, D., & Hornero, R. (2021). A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea. IEEE Journal of Biomedical and Health Informatics, 25(8), 2906–2916. https://doi.org/10.1109/JBHI.2020.3048901
Veauthier, C., Ryczewski, J., Mansow-Model, S., Otte, K., Kayser, B., Glos, M., Schöbel, C., Paul, F., Brandt, A. U., & Penzel, T. (2019). Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing. Scientific Reports, 9(1), 1–11. https://doi.org/10.1038/s41598-019-53050-3
Xue, B., Deng, B., Hong, H., Wang, Z., Zhu, X., & Feng, D. D. (2020). Non-Contact Sleep Stage Detection Using Canonical Correlation Analysis of Respiratory Sound. IEEE Journal of Biomedical and Health Informatics, 24(2), 614–625. https://doi.org/10.1109/JBHI.2019.2910566
Yan, X., Wang, L., Zhu, J., Wang, S., Zhang, Q., & Xin, Y. (2022, August). Automatic Obstructive Sleep Apnea Detection Based on Respiratory Parameters in Physiological Signals. In 2022 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 461-466). IEEE.
Yeh, C. Y., Chang, H. Y., Hu, J. Y., & Lin, C. C. (2022). Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network. Sensors, 22(2). https://doi.org/10.3390/s22020510
Yıldız, M. (2021). Uyku Apnesi Tespitinde Yenilikler. Natural & Applıed Scıences Journal, 3, 132–138.
Yue, H., Lin, Y., Wu, Y., Wang, Y., Li, Y., Guo, X., Huang, Y., Wen, W., Zhao, G., Pang, X., & Lei, W. (2021). Deep learning for diagnosis and classification of obstructive sleep apnea: A nasal airflow-based multi-resolution residual network. Nature and Science of Sleep, 13, 361–373. https://doi.org/10.2147/NSS.S297856
Zarei, A., & Asl, B. M. (2019). Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features from Single-Lead ECG Signal. IEEE Journal of Biomedical and Health Informatics, 23(3), 1011–1021. https://doi.org/10.1109/JBHI.2018.2842919
Zhang, Y., Chen, Y., Hu, L., Jiang, X., & Shen, J. (2017, November). An effective deep learning approach for unobtrusive sleep stage detection using microphone sensor. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 37-44). IEEE.
Zhu, J., Zhou, A., Gong, Q., Zhou, Y., Huang, J., & Chen, Z. (2022). Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest. Applied Sciences (Switzerland), 12(9). https://doi.org/10.3390/app12094218
Zhu, K., Li, M., Akbarian, S., Hafezi, M., Yadollahi, A., & Taati, B. (2019). Vision-Based Heart and Respiratory Rate Monitoring during Sleep-A Validation Study for the Population at Risk of Sleep Apnea. IEEE Journal of Translational Engineering in Health and Medicine, 7(October), 1–8. https://doi.org/10.1109/JTEHM.2019.2946147
Zhu, K., Yadollahi, A., & Taati, B. (2019, July). Non-contact apnea-hypopnea index estimation using near infrared video. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 792-795). IEEE.
Zhuang, Z., Wang, F., Yang, X., Zhang, L., Fu, C. H., Xu, J., Li, C., & Hong, H. (2022). Accurate contactless sleep apnea detection framework with signal processing and machine learning methods. Methods, 205(January 2022), 167–178. https://doi.org/10.1016/j.ymeth.2022.06.013

Ayrıntılar

Birincil Dil

Türkçe

Konular

Elektrik Mühendisliği

Bölüm

Derleme

Yazarlar

İsrafil Karadöl ^*
0000-0002-9239-0565
Türkiye

Yayımlanma Tarihi

15 Mart 2023

Gönderilme Tarihi

16 Kasım 2022

Kabul Tarihi

29 Kasım 2022

Yayımlandığı Sayı

Yıl 1970 Cilt: 26 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1205807

IZ

https://izlik.org/JA33AU38PF

Kaynak Göster

RIS / Bibtex

APA

Karadöl, İ. (2023). OBSTRÜKTİF UYKU APNESİ TESPİTİNDE POLİSOMNOGRAFİYE ALTERNATİF YENİ YÖNTEMLER. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 295-307. https://doi.org/10.17780/ksujes.1205807

Cited By

Determination of the risk of obstructive sleep apnea syndrome in individuals aged 18 years and above

Revista da Associação Médica Brasileira

https://doi.org/10.1590/1806-9282.20230968

OBSTRÜKTİF UYKU APNESİ TESPİTİNDE POLİSOMNOGRAFİYE ALTERNATİF YENİ YÖNTEMLER

NEW ALTERNATİVE METHODS TO POLYSOMNOGRAPHY İN THE DETECTİON OF OBSTRUCTİVE SLEEP APNEA

Abstract

Keywords