Radyal tabanlı fonksiyon ağlarını kullanarak EKG sinyallerinin sıkıştırılması

Year 2018, Volume: 20 Issue: 1, 503 - 513, 26.04.2018

Ömer Karal

https://doi.org/10.25092/baunfbed.418707

Abstract

Elektrokardiyogram (EKG), kalbin çalışması esnasında kalp kaslarında meydana gelen elektriksel aktivitelerin grafik olarak gösterimidir.  EKG, kalp hastalıklarının teşhisinde ve analizinde oldukça önemli bir rol oynamaktadır.  Herhangi bir kalp rahatsızlığına sahip kişilerin kalbinde meydana gelebilecek bir rahatsızlığı önceden tespit edebilmek için, EKG sinyalleri sürekli olarak kaydedilir, depolanır ve dijital iletişim ağları üzerinden iletilir.  Ancak bu tür kayıtlar ortamdan dolayı gürültüye maruz kalabilir.  Dahası, bu şekildeki kayıtlar depolama ve iletimi zorlaştıracak düzeyde büyük miktarda veri üretir.  Yukarıda sözü edilen nedenlerden dolayı gürültülü ortamda bile etkili bir EKG veri sıkıştırma modeli gereklidir.  Bu çalışma, EKG işaretlerinin doğal yapısını gürültülü ortamlarda bile korumak ve daha az sayıda parametre ile yeniden temsil etmek için Radyal Tabanlı Fonksiyon Ağlarını (RTFA) sunar.  RTFA’ların tasarımında, modelin yaklaşık doğruluğunu etkileyen önemli unsurlardan birisi olan radyal taban fonksiyonlarının merkezlerinin verimli bir şekilde belirlenmesidir.  Bu amaçla, k-means kümeleme algoritması kullanılmıştır. Yeniden yapılandırılmış EKG dalga biçimi, ortalama karesel hata, ortalama mutlak hata ve sıkıştırma oranı açısından niceliksel olarak değerlendirilmiştir. Tüm bu adımlar MATLAB ortamında uygulanmıştır.

Keywords

Elektrokardiogram, radyal tabanlı fonksiyon ağları, veri sıkıştırma

ECG signal compression using radial basis function networks

Abstract

An electrocardiogram (ECG) is the graphical representation of electrical activity in the cardiac muscles of the heart.  It plays a significant role in diagnosis and analysis of cardiac diseases.  In order to detect any cardiac diseases in advance, the ECG signals are continuously recorded, stored and transmitted over digital communication networks, but such records may be subject to noise due to environment.  Moreover, these types of records produce large amounts of data that will make storage and transmission difficult.  Due to the reasons mentioned above, an effective ECG data compression model is required even in a noisy environment.  This work presents Radial Basis Function Networks (RBFN) to preserve the natural structure of ECG signals even in noisy environments and to re-construct with fewer parameters.  In the design of RBFN, the center of the radial basis functions, which is one of the important factors affecting the approximate accuracy of the model, is to be determined efficiently.  For this purpose, k-means clustering algorithm is used in the paper.  The reconstructed ECG waveform was quantitatively evaluated in terms of root mean squared error, mean absolute error, and compression ratio. These steps are implemented in MATLAB environment.

Keywords

Electrocardiogram, radial basis function networks, compression

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