Kablosuz EKG Cihazı Tasarımı ve Sinyal İşleme Teknikleri Kullanılarak Özniteliklerin Değerlendirilmesine Yönelik Web Sitesi Tasarımı

Yıl 2021, Sayı: 26 - Ejosat Özel Sayı 2021 (HORA), 144 - 150, 31.07.2021

Sinem Abdioğlu Büşra Acar Ahmet Reşit Kavsaoğlu

https://doi.org/10.31590/ejosat.951988

Öz

Bu çalışmada, Wi-Fi haberleşme teknolojisi kullanılarak Elektrokardiyografi (EKG) sinyallerinin kablosuz olarak web ortamında görüntülenmesi ve gerekli sinyal işleme teknikleri kullanılarak bu sinyal hakkında yorum yapabilir dinamik bir sistem geliştirmek temel amaçtır. Bu doğrultuda ilk olarak Physionet.org web sayfasının sunduğu hazır EKG sinyalleri üzerinde çalışılmış ve bu sinyallerin öznitelik çıkarımları yapılmıştır. Aynı işlem AD8232 Kalp Atış Hızı Sensörü ile kaydedilen sinyaller üzerinde gerçekleştirilmiştir. Sinyaller işlenmeden önce sahip oldukları gürültülerden arındırılabilmesi için Kayan Ortalama Alma filtresinden geçirilmiştir. Elde edilen EKG sinyaline Pan-Tompkins algoritması uygulanmıştır. EKG sinyallerinin işlenmesi sonucu teşhis için kalp atım hızı ve kalp hız değişimi gibi öznitelikler elde edilmektedir. Çıkartılan bu öznitelikler erken teşhis ve tedavi imkânı sağlayabilmektedir. Bu çalışmada ortalama kalp atış hızına bağlı olarak ortaya çıkabilen Taşikardi ve Bradikardi durumlarının teşhisi gerçekleştirilmiş olup elde edilen sonuçlar web arayüzünde görüntülenmiştir. Bu sistemde web arayüzü, hasta ve doktor tipinde iki adet kullanıcı seçeneğine hizmet sunmaktadır. Hasta, kendine ait bütün ölçümleri; ölçümün gerçekleştirildiği tarih ve saat, ölçüm sonucu ortaya çıkan ortalama kalp atış hızı ve bu sayının standartlara göre yorumlanmış hali (Taşikardi, Brakidardi veya Normal) ve hastanın kendisine ait detayları (T.C. Kimlik Numarası, Telefon Numarası, Mail Adresi vb.) bu sistemde görüntüleyebilmektedir. Hastalar kalp atış hızı hakkında düzenli ve doğru bilgiye ulaşabilmekte ve gereksiz doktor randevuları bu sistem sayesinde ortadan kaldırabilmektedir. Doktor, randevuya gelen hastanın kalp sağlığı hakkında bazı tetkiklere gerek duymadan güvenilir ön bilgiye sahip olabilmektedir. Bu durum doktora zaman kazandırabilmekte ve daha fazla hastaya hizmet vermesine olanak sağlayabilmektedir.

Anahtar Kelimeler

Kablosuz haberleşme, EKG, Sinyal işleme, Öznitelik çıkarımı, Pan-Tompkins algoritması

Wireless ECG Device Design and Website Design for the Evaluation of Features Using Signal Processing Techniques

Öz

In this study, the main objective is to display Electrocardiography (ECG) signals wirelessly on the web using Wi-Fi communication technology and to develop a dynamic system that can interpret this signal by using the necessary signal processing techniques. In this direction, firstly, the ECG signals provided by the Physionet.org web page were studied and the feature extractions of these signals were made. The same process was performed on the signals recorded with the AD8232 Heart Rate Sensor. Before the signals are processed, they are passed through the Moving Averaging filter to remove any noise they may have. Pan-Tompkins algorithm is applied to the obtained ECG signal. As a result of processing ECG signals, features such as heart rate and heart rate change are obtained for diagnosis. These extracted features can provide early diagnosis and treatment opportunities. In this study, the diagnosis of Tachycardia and Bradycardia, which may occur depending on the average heart rate, was performed and the results were displayed on the web interface. In this system, the web interface serves for two user types as patient and doctor. All owned measurements; the date and time of the measurement, the average heart rate resulting from the measurement and the interpretation (Tachycardia, Brachydardy or Normal) of this number according to the standards and the patient's own details (T.R. Identity Number, Telephone Number, Mail Address, etc.) can be seen by the patient within this system. Patients can access regular and accurate information about heart rate. Unnecessary doctor appointments can be eliminated with the aid of this system. The doctor can have reliable preliminary information about the heart situation of the patient who comes to the appointment without the need for some examinations. This can save the doctor time to serve more patients.

Anahtar Kelimeler

Wireless communication, ECG, Signal processing, Feature extraction, Pan-Tompkins algorithm

Kaynakça

• Alafeef, M. (2017). Smartphone-based photoplethysmographic imaging for heart rate monitoring. Journal of medical engineering & technology, 41(5), 387-395.
• Al-Zaiti, S.S., Shusterman, V., Carey, M.G. (2013). Novel technical solutions for wireless ECG transmission & analysis in the age of the internet cloud, Journal of Electrocardiology, 46(6), 540–54. Andreadis, I. I., & Nikita, K. S. (2019). Tele-, Mobile-and Web-Based Technologies in Cardiovascular Medicine. In Cardiovascular Computing—Methodologies and Clinical Applications (pp. 261-277). Springer, Singapore.
• Cao, K., Hu, T., Li, Z., Zhao, G., & Qian, X. (2021). Deep multi-task learning model for time series prediction in wireless communication. Physical Communication, 44(December). https://doi.org/10.1016/j.phycom.2020.101251
• Chen, C. M. (2011). Web-based remote human pulse monitoring system with intelligent data analysis for home health care. Expert Systems with Applications, 38(3).
• Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C.H., Mark RG, Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23): e215-e220
• Hussein, A. F., Burbano-Fernandez, M., Ramírez-González, G., Abdulhay, E., & De Albuquerque, V. H. C. (2018). An automated remote cloud-based heart rate variability monitoring system. IEEE Access, 6, 77055-77064.
• Jenkins, A.C., Mitchell, R. D. Sarazan. (2008). Noninvasive ECG telemetry using Bluetooth® technology in concious nonhuman primates in a toxicology study setting, Journal of Pharmacological and Toxicological Methods, 461, 152-153
• Jong, G. J., & Horng, G. J. (2017). Fuzzy Inference Engine Integrated with Blood Pressure and Heart Variability for Medical Web Platform. Wireless Personal Communications, 92(4), 1695-1712.
• Kahani, N., Elgazzar, K., & Cordy, J. R. (2016, April). Authentication and access control in e-health systems in the cloud. In 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS) (pp. 13-23). IEEE.
• Khan, M. M., Karim, R. (2020, December). Development of Smart e-Health System for COVID-19 Pandemic. In 2020 23rd International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
• Kirbaş, İ., & Bayilmiş, C. (2012). HealthFace: A web-based remote monitoring interface for medical healthcare systems based on a wireless body area sensor network. Turkish Journal of Electrical Engineering & Computer Sciences, 20(4), 629-638.
• Kirtana, R. N., & Lokeswari, Y. V. (2017, January). An IoT based remote HRV monitoring system for hypertensive patients. In 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP) (pp. 1-6). IEEE.
• Kong, D., Zhu, J., Wu, S., Duan, C., Lu, L., & Chen, D. (2019). A novel IRBF-RVM model for diagnosis of atrial fibrillation. Computer Methods and Programs in Biomedicine, 177, 183–192. https://doi.org/10.1016/j.cmpb.2019.05.028
• Li, Y., Qu, Q., Wang, M., Yu, L., Wang, J., Shen, L., & He, K. (2020). Deep learning for digitizing highly noisy paper-based ECG records. Computers in Biology and Medicine, 127(October), 104077. https://doi.org/10.1016/j.compbiomed.2020.104077
• Mazaheri, V., & Khodadadi, H. (2020). Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm. Expert Systems with Applications, 161(December), 1–39. https://doi.org/10.1016/j.eswa.2020.113697
• Miao, F., Wen, B., Hu, Z., Fortino, G., Wang, X. P., Liu, Z. D., Tang, M., & Li, Y. (2020). Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques. Artificial Intelligence in Medicine, 108(August), 1–29. https://doi.org/10.1016/j.artmed.2020.101919
• Molina, E., Torres, C. E. S., Salazar-Cabrera, R., López, D. M., & Vargas-Cañas, R. (2020). Intelligent telehealth system to support epilepsy diagnosis. Journal of Multidisciplinary Healthcare, 13, 433.
• Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE transactions on biomedical engineering, (3), 230-236.
• Quintero, L., Papapetrou, P., Muñoz, J. E., & Fors, U. (2019, November). Implementation of Mobile-Based Real-Time Heart Rate Variability Detection for Personalized Healthcare. In 2019 International Conference on Data Mining Workshops (ICDMW) (pp. 838-846). IEEE.
• Raj, C., Jain, C., & Arif, W. (2017, March). HEMAN: Health monitoring and nous: An IoT based e-health care system for remote telemedicine. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2115-2119). IEEE.
• Sengan, S., Kamalam, G. K., Vellingiri, J., Gopal, J., Velayutham, P., & Subramaniyaswamy, V. (2020). Medical information retrieval systems for e-Health care records using fuzzy based machine learning model. Microprocessors and Microsystems, 103344.
• Kho, T. K., Besar, R., Tan, Y. S., Tee, K. H. & Ong, K. C. “Bluetooth-enabled ECG Monitoring System”, in TENCON, 2005, pp. 1-5, 21-24 Nov. 2005.
• Taştan, M. (2018). Iot based wearable smart health monitoring system. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 14(3), 343-350.
• Rajendra, U., AcharyaJasjit S., SuriJos A., SpaanShankar, E., Krishnan, M. (2007). Advances in Cardiac Signal Processing, SpringerLink.com, 441, 121-165. https://link.springer.com/book/10.1007/978-3-540-36675-1#toc Yazgan, E., & Korürek, M. (1996). Tıp Elektroniği. 373, 90-102.
• Zhang, Q., Zhou, D., & Zeng, X. (2017). Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomedical engineering online, 16(1), 1-20.