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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Modeling of ECG and SCG Signals Using Predefined Signature and Envelope Sets

EKG ve SKG Sinyallerinin Temel Tanım ve Zarf Vektörleriyle Modellenmesi

How to cite: Hardal E, Zaim Gökbay . Modeling of ecg and scg signals using predefined signature and envelope sets. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2020; 3(2): 77-83. DOI: 10.54856/jiswa.202012123

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Title: Modeling of ECG and SCG Signals Using Predefined Signature and Envelope Sets

Abstract: Seismocardiogram (SCG) is a low-cost monitoring method to collect precordial vibrations of sternum due to heartbeats and evaluate cardiac activity. It is mostly used as an auxiliary measurement to the other monitoring methods; however, it carries significant patterns reflecting current cardiovascular health status of subjects. If it is properly collected within a non-clinical environment, it might be able to present preliminary data to physicians before clinic. SCG signals are morphologically noisy. These signals store excessive amount of data. Extracting significant information corresponding to heartbeat complexes is so important. Previously, the method called compressed sensing (CS) had been applied to weed up the redundant information by taking the advantage of sparsity feature in a study. This compressed sensing is based on storing significant signals below the Nyquist rate which suffice for medical diagnosis. It has been feasible to compress SCG signals with 3:1 compression rate at least while maintaining accurate signal reconstruction. Nevertheless, higher compression rates lead to the formation of artifacts on reconstructed signals. This limits a more aggressive compression to reduce the amount of data. The requirement of a different approach which will allow higher compression rates and lower loss of information arises. The purpose of this study is to obtain more competent results by using a method called predefined signature and envelope vector sets (PSEVS) which has been satisfyingly applied to electrocardiogram (ECG) and speech signals. In the study, simultaneously recorded ECG and SCG signals were modeled with the method called PSEVS. The reconstructed signals were compared to the original signals so as to investigate the efficacy of signature-based modeling methods in constructing medically remarkable biosignals for clinical use. After examining the components of reconstructed signals called frame-scaling coefficient, signature and envelope vectors, it has been seen that the error function values of envelope vectors differ from expected values. We concluded that reconstructed SCG signals were not adequate for medical diagnosis.

Keywords: Electrocardiogram (ECG); seismocardiogram (SCG); predefined signature and envelope sets; bio-signal processing


Başlık: EKG ve SKG Sinyallerinin Temel Tanım ve Zarf Vektörleriyle Modellenmesi

Özet: Sismokardiyogram (SKG), göğüs kafesinde oluşan kalp atışı kaynaklı titreşimleri kaydedip değerlendirmek için kullanılan düşük maliyetli bir izleme yöntemidir. Genellikle yardımcı izleme yönetimi olarak kullanılmasına rağmen, kişinin kardiyovasküler sağlık durumuna dair önemli bilgiler içerir. Eğer bu bilgiler klinik-dışı ortamda doğru şekilde toplanabilirse, sağlık profesyonellerine klinik öncesi ön bilgi sağlanabilir. Fakat SKG sinyalleri doğası gereği gürültülüdür. Bu sebeple, sinyallerin içinden kalp atışına dair anlamlı bilgiyi ayıklamak büyük önem arz eder. Daha önce bu amaçla uygulanan ve Nyquist oranının altında kalan tıbbi teşhise uygun sinyalleri ayıklayan yöntemle, sinyal kalitesini bozmadan 3’te 1 oranında veri sıkıştırması mümkün hale getirilmiştir. Daha yüksek oranlara çıkıldıkça yeniden inşa edilen sinyallerde ise bozulma gözlenmiştir. Bu problemi gidermek amacıyla, daha düşük veri kaybıyla daha yüksek sıkıştırma oranı sağlayan bir yönteme gereklilik duyulmaktadır. Bu çalışmanın amacı, daha önce elektrokardiyogram (EKG) ve ses sinyallerine başarıyla uygulanan temel tanım ve zarf vektörleri yöntemiyle daha iyi sonuçlar elde etmektir. Bu çalışmada tıbbi teşhise uygun biyo-sinyaller elde etmek amacıyla, EKG ve SKG sinyalleri temel tanım ve zarf vektörleriyle modellenmiştir. Yeniden inşa edilen sinyaller, orijinal sinyallerle kıyaslanmıştır. Yeniden inşa edilen sinyallerin, çerçeve ölçekleme katsayısı, temel tanım ve zarf vektörleri olarak adlandırılan bileşenlerinin global hata fonksiyonu değerlerinin beklenenden farklı çıktığı görülmüştür. Modelleme sonucunda maalesef tıbbi teşhise uygun SKG sinyalleri elde edilememiştir.

Anahtar kelimeler: Elektrokardiyogram (EKG); sismokardiyogram (SKG); temel tanım ve zarf vektörleri; biyo-sinyal işleme


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