Araştırma Makalesi
BibTex RIS Kaynak Göster

Deneyap kart kullanarak pozisyonel uyku apnesi tespiti ve IoT uygulaması

Yıl 2023, Cilt: 13 Sayı: 4, 1033 - 1045, 15.10.2023

Harun Sümbül

https://doi.org/10.17714/gumusfenbil.1262913

Öz

Bu çalışmada, kalp-damar hastalıkları için risk oluşturabilen, hava yollarını tıkayan ve soluma ile ilgili en yaygın hastalıklardan olan Uyku Apnesi hastalığına tanı koymada kullanılabilecek non-invaziv bir tanı yöntemi geliştirilmiştir. Bu uygulama için yerli – milli imkanlar ile geliştirilen Deneyap Kart kullanılmıştır. Pozisyon ve apne tespitinde Deneyap kart üzerinde dahili olarak bulunan üç eksenli IMU ivmeölçer sensörü (LSM6DSM) kullanılmıştır. Uyku apnesi hastalığının test ölçümlerinin gerçekleştirileceği sembolik ama gerçeğe uygun bir ortam oluşturulmuştur. Bu kapsamda ölçümlerin yapılabilmesi için plastik bir bebek maket modeli kullanılmıştır. Yapılan çalışma neticesinde hasta yatma pozisyonu, hangi yatma pozisyonda kaç dakika kaldığı, gece boyunca ne kadar pozisyon değiştirdiği, hangi pozisyonda apneye girdiği gibi hastaya ait birçok parametre başarılı bir şekilde ölçülmüş ve SD karta kaydedilmiştir. Ölçülen parametrelerin uzaktan izlenmesine imkân sağlayacak nesnelerin interneti (IoT) temelli bir sistem geliştirilerek veriler farklı ortamlara iletilerek başarılı bir şekilde izlenebilmiştir. Bu çalışma ile yerli-milli kartımız olan Deneyap Kart kullanarak bundan sonraki çalışmalara ışık tutabilecek öznel bir çalışma literatüre kazandırılmıştır.

Anahtar Kelimeler

Deneyap kart Nesnelerin interneti Uyku apnesi Yatış pozisyonu

Destekleyen Kurum

Ondokuz Mayıs Üniversitesi Bilimsel Araştirma Projesi Koordinatörlüğü

Proje Numarası

PYO.YMY.1908.22.002

Teşekkür

Bu çalışma, Ondokuz Mayıs Üniversitesi'nin Bilimsel Araştırma Projeleri (BAP) koordinatörlüğü tarafından PYO.YMY.1908.22.002 numaralı proje ile finanse edilmiştir. Makale, inceleme ve değerlendirme aşamalarında Gümüşhane Üniversitesi Fen Bilimleri Dergisi editör ve hakemlerinin yapmış olduğu katkılardan dolayı teşekkür ediyoruz.

Kaynakça

  • Adafruit. (2023). https://io.adafruit.com/harunsumbul/wippersnapper
  • Ardıç, S., Demir, A. U., Hikmet, F., Oktay, B., Darılmaz, Y. G., Zübeyir, Y., Pınar, A., Cengiz, Ö., & Bardakçı, M. Ġ. (2015). Chronic obstructive pulmonary disease and obstructive sleep apnea symptoms: an outpatient-based population study in Turkey. Turkish Journal of Geriatrics, 15(2),142-150.
  • Chen, E. X., Chen, Y., Ma, W., Fan, X., & Li, Y. (2022). Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowledge-Based Systems, 247, 108783. https://doi.org/10.1016/j.knosys.2022.108783
  • Demir, A.K., & Abut, F. (2018). Grid ağ topolojilerinde CoAP ve CoCoA tıkanıklık kontrol mekanizmalarının karşılaştırılması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 53-60. https://doi.org/10.17714/gumusfenbil.436056
  • Deneyap Kart. (2023) https://docs.deneyapkart.org/tr/content/contentDetail/deneyap-kart
  • Genç, Y. (2023), https://www.medikalakademi.com.tr/bebeklerde-uyku-pozisyonu-nasil-olmali/
  • Hassan, O., Paul, T., Shuvo, M.H., Parvin, D., Thakker, R., Chen, M., Mosa, A. S. M., & Islam, S. K. (2022). Energy efficient deep learning inference embedded on FPGA for sleep apnea detection. Journal of Signal Processing Systems, 94, 609–619. https://doi.org/10.1007/s11265-021-01722-7
  • Iber, C. (2007). The AASM manual for the scoring of sleep ve associated events : rules. terminology and technical Specification, https://ci.nii.ac.jp/naid/10024500923.
  • Kaimakamis, E., Bratsas, C., Sichletidis, L., Karvounis, C., & Maglaveras N. (2009). Screening of patients with obstructive sleep apnea syndrome using C4.5 algorithm based on nonlinear analysis of respiratory signals during sleep. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3465-3469. http://dx.doi.org/10.1109/IEMBS.2009.5334605.
  • Komada, Y., Takaesu, Y., Nishida, S., Sasai, T., Furudate, N., & Inoue, Y. (2013). Comparison of clinical features between primary and secondary sleep-related eating syndrome. Sleep Medicine, 14S, e165–e238. https://doi.org/10.1016/j.sleep.2013.11.404
  • Mallegni, N., Molinari, G., Ricci, C., Lazzeri, A., Rosa, D. L., Crivello, A., & Milazzo, M. (2022). Sensing devices for detecting and processing acoustic signals in healthcare. Biosensors, 12(10), 835. https://doi.org/10.3390/bios12100835
  • Oral, O., Bilgin, S., & Ak, M. U. (2022). Evaluation of vibration signals measured by 3-Axis MEMS accelerometer on human face using wavelet transform and classifications. Tehnički vjesnik, 29(2), 355-362. https://doi.org/10.17559/TV-20210820150837
  • Pombo, N., Silva, B. M. C., Pinho, A. M., & Nuno Garcia. (2020). Classifier precision analysis for seep apnea detection using ECG signals. IEEE Access, 8, 200477-200485. https://doi.org/10.1109/ACCESS.2020.3036024
  • Rasche, K., Duchna, H. W., Lauer, J., Orth, M., Kotterba, S., Bauer, T. T., Gillissen, A., & Schultze-Werninghaus, G. (1999). Obstructive sleep apnea and hypopnea efficacy and safety of a long-acting beta2-agonist. Sleep and Breathing, 3(4),125–129. https://doi.org/10.1007/s11325-999-0125-1
  • Sümbül, H., & Yüzer A. H. (2016). 3D monitoring of lying position for patients with positional sleep apnea syndrome. Journal of New Results in Science, 12(2016), 59–76. http://dergipark.gov.tr/jnrs/issue/27333/287730
  • Sümbül, H., & Yüzer A. H. (2015). Measuring of diaphragm movements by using iMEMS acceleration sensor. International Conference on Electrical and Electronics Enginering (ELECO 2015), Bursa, Turkey, 166-170. https://doi.org/10.1109/ELECO.2015.7394532
  • Sümbül, H., Yüzer, A.H., & Şekeroğlu, K. (2022). A novel portable real-time low-cost sleep apnea monitoring system based on the global system for mobile communications (GSM) network. Medical & Biological Engineering & Computing, 60, 619–632. https://doi.org/10.1007/s11517-021-02492-x
  • Teofilo L., & Lee-Chiong, Jr. (2003). Monitoring respiration during sleep. Clinics in Chest Medicine, 24(2), 297-306, https://doi.org/ 10.1016/s0272-5231(03)00021-2.
  • Uriel, M. H., Benjamin, M., Tareq, A., Leen, J., James, M. & Dingguo, Z. (2021). Wearable assistive robotics: a perspective on current challenges and future trends. Sensors, 21(20),6751. https://doi.org/10.3390/s21206751
  • Uykuder. (2023). https://www.ntv.com.tr/saglik/turkiyede-1-5-milyon-kisinin-uykusu-bozuk,A1xsz8gyOUqh-Ppyq3D-KQ
  • Varady, P., Bongar, S., & Benyo, Z. (2003). Detection of airway obstructions and sleep apnea by analyzing the phase relation of respiration movement signals. IEEE Transactıons On Instrumentatıon And Measurement, 52(1),2-6. https://doi.org/10.1109/TIM.2003.809095
  • Wisana, I. D. G. H., Nugraha, P. C., & Estiwidani, D. (2021). The effectiveness obstructive sleep apnea monitoring using telemedicine smartphone system (TmSS). Journal of Biomimetics, Biomaterials and Biomedical Engineering, 50, 113–121. https://doi.org/10.4028/www.scientific.net/jbbbe.50.113
  • Xie, B., & Minn, H. (2012). Real-time sleep apnea detection by classifier combination. IEEE Transactions On Information Technology In Biomedicine, 16(3),469-477. https://doi.org/10.1109/TITB.2012.2188299
  • Xu, J., & Yuan, K. (2021). Wearable muscle movement information measuring device based on acceleration sensor. Measurement. 167(108274). https://doi.org/10.1016/j.measurement.2020.108274
  • Yüzer, A. H., Sümbül, H., & Polat, K. (2020). A novel wearable real-time sleep apnea detection system based on the acceleration sensor. IRBM Innovation and Research in BioMedical engineering, 41(1), 39-47. https://doi.org/10.1016/j.irbm.2019.10.007
  • Yüzer, A. H., Sumbul, H., Polat, K., & Nour, Majid. (2020). A different sleep apnea classification system with neural network based on the acceleration signals. Applied Acoustics, 163,107225. https://doi.org/10.1016/j.apacoust.2020.107225

Positional sleep apnea detection and IoT application by using Deneyap card

Yıl 2023, Cilt: 13 Sayı: 4, 1033 - 1045, 15.10.2023

Harun Sümbül

https://doi.org/10.17714/gumusfenbil.1262913

Öz

In this study, a non-invasive diagnosis that can be used to diagnose Sleep Apnea, which may pose a risk for cardiovascular diseases, obstruct airways and are the most common diseases related to breathing method has been developed. For this application, the Deneyap Card, which was developed with local and national facilities, was used. Three-axis IMU accelerometer sensor (LSM6DSM), which is built into the Deneyap card, is used as a detector. A symbolic but realistic environment has been created in which test measurements of sleep apnea disease will be carried out. In this context, a plastic doll model was used to make the measurements. As a result of the study, many parameters of the patient such as the patient's position, how long he lay in which position, how many times he changed positions during sleep, and in which position he entered apnea were successfully measured and recorded on the SD card. By developing an Internet of Things (IoT)-based system that will allow the remote monitoring of the measured parameters, the data can be successfully monitored by transmitting it to different environments. With this study, a subjective study that can shed light on future studies has been brought to the literature by using our local-national card, the Deneyap Card.

Anahtar Kelimeler

Deneyap card Internet of Things Sleep apnea Lying position

Proje Numarası

PYO.YMY.1908.22.002

Kaynakça

  • Adafruit. (2023). https://io.adafruit.com/harunsumbul/wippersnapper
  • Ardıç, S., Demir, A. U., Hikmet, F., Oktay, B., Darılmaz, Y. G., Zübeyir, Y., Pınar, A., Cengiz, Ö., & Bardakçı, M. Ġ. (2015). Chronic obstructive pulmonary disease and obstructive sleep apnea symptoms: an outpatient-based population study in Turkey. Turkish Journal of Geriatrics, 15(2),142-150.
  • Chen, E. X., Chen, Y., Ma, W., Fan, X., & Li, Y. (2022). Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowledge-Based Systems, 247, 108783. https://doi.org/10.1016/j.knosys.2022.108783
  • Demir, A.K., & Abut, F. (2018). Grid ağ topolojilerinde CoAP ve CoCoA tıkanıklık kontrol mekanizmalarının karşılaştırılması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 53-60. https://doi.org/10.17714/gumusfenbil.436056
  • Deneyap Kart. (2023) https://docs.deneyapkart.org/tr/content/contentDetail/deneyap-kart
  • Genç, Y. (2023), https://www.medikalakademi.com.tr/bebeklerde-uyku-pozisyonu-nasil-olmali/
  • Hassan, O., Paul, T., Shuvo, M.H., Parvin, D., Thakker, R., Chen, M., Mosa, A. S. M., & Islam, S. K. (2022). Energy efficient deep learning inference embedded on FPGA for sleep apnea detection. Journal of Signal Processing Systems, 94, 609–619. https://doi.org/10.1007/s11265-021-01722-7
  • Iber, C. (2007). The AASM manual for the scoring of sleep ve associated events : rules. terminology and technical Specification, https://ci.nii.ac.jp/naid/10024500923.
  • Kaimakamis, E., Bratsas, C., Sichletidis, L., Karvounis, C., & Maglaveras N. (2009). Screening of patients with obstructive sleep apnea syndrome using C4.5 algorithm based on nonlinear analysis of respiratory signals during sleep. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3465-3469. http://dx.doi.org/10.1109/IEMBS.2009.5334605.
  • Komada, Y., Takaesu, Y., Nishida, S., Sasai, T., Furudate, N., & Inoue, Y. (2013). Comparison of clinical features between primary and secondary sleep-related eating syndrome. Sleep Medicine, 14S, e165–e238. https://doi.org/10.1016/j.sleep.2013.11.404
  • Mallegni, N., Molinari, G., Ricci, C., Lazzeri, A., Rosa, D. L., Crivello, A., & Milazzo, M. (2022). Sensing devices for detecting and processing acoustic signals in healthcare. Biosensors, 12(10), 835. https://doi.org/10.3390/bios12100835
  • Oral, O., Bilgin, S., & Ak, M. U. (2022). Evaluation of vibration signals measured by 3-Axis MEMS accelerometer on human face using wavelet transform and classifications. Tehnički vjesnik, 29(2), 355-362. https://doi.org/10.17559/TV-20210820150837
  • Pombo, N., Silva, B. M. C., Pinho, A. M., & Nuno Garcia. (2020). Classifier precision analysis for seep apnea detection using ECG signals. IEEE Access, 8, 200477-200485. https://doi.org/10.1109/ACCESS.2020.3036024
  • Rasche, K., Duchna, H. W., Lauer, J., Orth, M., Kotterba, S., Bauer, T. T., Gillissen, A., & Schultze-Werninghaus, G. (1999). Obstructive sleep apnea and hypopnea efficacy and safety of a long-acting beta2-agonist. Sleep and Breathing, 3(4),125–129. https://doi.org/10.1007/s11325-999-0125-1
  • Sümbül, H., & Yüzer A. H. (2016). 3D monitoring of lying position for patients with positional sleep apnea syndrome. Journal of New Results in Science, 12(2016), 59–76. http://dergipark.gov.tr/jnrs/issue/27333/287730
  • Sümbül, H., & Yüzer A. H. (2015). Measuring of diaphragm movements by using iMEMS acceleration sensor. International Conference on Electrical and Electronics Enginering (ELECO 2015), Bursa, Turkey, 166-170. https://doi.org/10.1109/ELECO.2015.7394532
  • Sümbül, H., Yüzer, A.H., & Şekeroğlu, K. (2022). A novel portable real-time low-cost sleep apnea monitoring system based on the global system for mobile communications (GSM) network. Medical & Biological Engineering & Computing, 60, 619–632. https://doi.org/10.1007/s11517-021-02492-x
  • Teofilo L., & Lee-Chiong, Jr. (2003). Monitoring respiration during sleep. Clinics in Chest Medicine, 24(2), 297-306, https://doi.org/ 10.1016/s0272-5231(03)00021-2.
  • Uriel, M. H., Benjamin, M., Tareq, A., Leen, J., James, M. & Dingguo, Z. (2021). Wearable assistive robotics: a perspective on current challenges and future trends. Sensors, 21(20),6751. https://doi.org/10.3390/s21206751
  • Uykuder. (2023). https://www.ntv.com.tr/saglik/turkiyede-1-5-milyon-kisinin-uykusu-bozuk,A1xsz8gyOUqh-Ppyq3D-KQ
  • Varady, P., Bongar, S., & Benyo, Z. (2003). Detection of airway obstructions and sleep apnea by analyzing the phase relation of respiration movement signals. IEEE Transactıons On Instrumentatıon And Measurement, 52(1),2-6. https://doi.org/10.1109/TIM.2003.809095
  • Wisana, I. D. G. H., Nugraha, P. C., & Estiwidani, D. (2021). The effectiveness obstructive sleep apnea monitoring using telemedicine smartphone system (TmSS). Journal of Biomimetics, Biomaterials and Biomedical Engineering, 50, 113–121. https://doi.org/10.4028/www.scientific.net/jbbbe.50.113
  • Xie, B., & Minn, H. (2012). Real-time sleep apnea detection by classifier combination. IEEE Transactions On Information Technology In Biomedicine, 16(3),469-477. https://doi.org/10.1109/TITB.2012.2188299
  • Xu, J., & Yuan, K. (2021). Wearable muscle movement information measuring device based on acceleration sensor. Measurement. 167(108274). https://doi.org/10.1016/j.measurement.2020.108274
  • Yüzer, A. H., Sümbül, H., & Polat, K. (2020). A novel wearable real-time sleep apnea detection system based on the acceleration sensor. IRBM Innovation and Research in BioMedical engineering, 41(1), 39-47. https://doi.org/10.1016/j.irbm.2019.10.007
  • Yüzer, A. H., Sumbul, H., Polat, K., & Nour, Majid. (2020). A different sleep apnea classification system with neural network based on the acceleration signals. Applied Acoustics, 163,107225. https://doi.org/10.1016/j.apacoust.2020.107225
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Harun Sümbül 0000-0001-5135-3410

Proje Numarası PYO.YMY.1908.22.002
Yayımlanma Tarihi 15 Ekim 2023
Gönderilme Tarihi 9 Mart 2023
Kabul Tarihi 11 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 4

Kaynak Göster

APA Sümbül, H. (2023). Deneyap kart kullanarak pozisyonel uyku apnesi tespiti ve IoT uygulaması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(4), 1033-1045. https://doi.org/10.17714/gumusfenbil.1262913
 Kapak Resmi İndir