Elektrokardiyografi Yardımıyla Hipertansiyonun Otomatik Belirlenmesinde Ampirik Kip Ayrışımının Gürültülü ve Gürültüsüz Sinyaller Üzerindeki Performansının Karşılaştırılması

Year 2022, Volume: 9 Issue: 2, 788 - 800, 31.05.2022

Yunus Emre Erdoğan , Ali Narin

https://doi.org/10.31202/ecjse.1009456

Abstract

Hipertansiyon (HPT), kalpten vücuda taşınan kanın atardamar duvarlarına uyguladığı kuvvetin, bazı hastalıklara sebebiyet verecek kadar yüksek olduğu duruma verilen isimdir. HPT’ye bağlı hastalıklar sonucunda her yıl dünyada birçok insan hayatını kaybetmektedir. Bu sebepten dolayı HPT’nin erken teşhis edilmesi oldukça kritik bir öneme sahiptir. Bu çalışma elektrokardiyogram (EKG) sinyalleri kullanılarak HPT hastalarının otomatik ve en az hata ile tespit edilmesi amacıyla yapılmıştır. Bu çalışmada EKG sinyalleri 4 farklı grupta toplanmıştır. Bu yaklaşımlar sırasıyla, normalize edilmiş gürültülü EKG sinyalleri, normalize edilmiş gürültüsüz EKG sinyalleri, normalize edilmemiş gürültülü EKG sinyalleri ve son olarak normalize edilmemiş gürültüsüz EKG sinyalleridir. Ampirik Kip Ayrışımı metodu vasıtasıyla 5 katmanlı iç mod fonksiyon (İMF) sinyalleri üzerinden elde edilen entropi ölçümleri yardımıyla HPT belirleme performansı analiz edilmiştir. Her bir İMF için elde edilen iki adet öznitelikle başarımlar mukayese edilmiştir. Özetle, 10-kat çapraz doğrulama metodundan yararlanılarak destek vektör makineleri (DVM) algoritmaları ile en yüksek doğruluk değeri %99,99 olarak elde edilmiştir. Elde edilen yüksek performanslardan, HPT hastası kişilerin belirlenmesinde doktorlara yararlı olacağı sonucuna varılabilir.

Keywords

Hipertansiyon, EKG, Ampirik Kip Ayrışımı, Destek Vektör Makineleri

Comparison of Performance of Empirical Mode Decomposition on Noisy and Noiseless Signals in Automatic Detection of Hypertension with the Assistance of Electrocardiography

Abstract

Hypertension (HPT) is the name given to the condition in which the force exerted by the blood carried from the heart to the body is high enough to cause some diseases. As a result of HPT-related diseases, many people die every year around the world. For this reason, early diagnosis of HPT is of critical importance. This study was conducted to detect HPT patients automatically and with minimum error by using electrocardiogram (ECG) signals. In this study, ECG signals were collected in 4 different groups. These approaches are respectively normalized noisy ECG signals, normalized noiseless ECG signals, unnormalized noisy ECG signals and finally unnormalized noiseless ECG signals. HPT determination performance was analyzed with the help of entropy measurements obtained from 5-layer internal mode function (IMF) signals by means of the Empirical Mode Decomposition method. Performances were compared with two features obtained for each IMF. In summary, the highest accuracy value of 99.99% was obtained with support vector machines (SVM) algorithms by using the 10-fold cross validation method. From the high performances obtained, it can be concluded that it will be useful to doctors in identifying people with HPT.

Keywords

Hypertension, ECG, Empirical Mode Decomposition, Support Vector Machines

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