SAMPLE ENTROPY ANALYSIS OF HEART RATE VARIABILITY IN RR INTERVAL DETECTION

Yıl 2020, Cilt: 8 Sayı: 3, 783 - 790, 24.09.2020

Veysel Alcan

https://doi.org/10.21923/jesd.745275

Öz

Entropy is a robust method that is able to measure irregularities or the general behavior of the complex time series which could be continuously interact with many different and independent factors in time. This study aimed to investigate the sample entropy measurement of heart rate variability (HRV) for evaluating of 50Hz interference and baseline wander (BW) noise effects on RR interval. Three different synthetic electrocardiogram (ECG) signals were recorded using the simulator device. Sample Entropy (SampEn) values of full length and windowed length of data were calculated to track and identify RR intervals. It was found that adult normal sinus rhythm (NSR) signal without noise had the most regular and consistent results while adult ECG signal with BW noisy had the most irregular and inconsistent results. Furthermore, the BW noisy had more effect on irregularity ECG signal than 50 Hz interference. Consequently, the SampEn provided the measurement of irregularity and randomness of ECG data. However, it was found that the determination of RR intervals for classification and decision support systems was not practical in real-time analysis of HRV from raw ECG recordings because of noisy affect.

Anahtar Kelimeler

sample entropy, heart rate variability, noisy factor, irregularity.

RR INTERVAL TESPİTİNDE KALP ATIM HIZI DEĞİŞKENLİĞİNİN ÖRNEK ENTROPİ ANALİZİ

Öz

Entropi, zaman içinde birçok farklı ve bağımsız faktörlerle sürekli olarak etkileşime girebilecek karmaşık zaman serilerinin düzensizliklerini veya genel davranışlarını ölçebilen sağlam bir yöntemdir. Bu çalışma, 50Hz gürültüsünün ve taban hattı kayması (BW) gürültüsünün RR aralığı üzerindeki etkilerini değerlendirmek için kalp hızı değişkenliğinin (HRV) örnek entropi (SampEn) ölçümünü araştırmayı amaçlamıştır. Stimülatör cihazı kullanılarak üç farklı sentetik elektrokardiyogram (EKG) sinyali kaydedilmiştir. RR aralıklarını izlemek ve tanımlamak için tam uzunluktaki ve pencereli veri uzunluğundaki SampEn değerleri hesaplanmıştır. BW gürültüsüne sahip yetişkin EKG sinyali en düzensiz ve tutarsız sonuçlara sahipken Gürültüsüz erişkin Normal Sinüs Ritim (NSR) sinyalinin en düzenli ve tutarlı sonuçlara sahip olduğu bulunmuştur. Ayrıca, EKG sinyali üzerindeki düzensizliklerde BW gürültüsü 50 Hz gürültüsüne göre daha fazla etkiye sahipti. Sonuç olarak, SampEn, EKG verilerinin düzensizliği ve rasgeleliğinin ölçülmesini sağlamıştır. Bununla birlikte, sınıflandırma ve karar destek sistemleri için RR aralıklarının belirlenmesi, gürültülü etki nedeniyle ham EKG kayıtlarından HRV'nin gerçek zamanlı analizinde pratik olmadığı bulunmuştur.

Anahtar Kelimeler

örnek entropy, kalp hızı değişkenliği, gürültü faktörü, düzensizlik

Kaynakça

• Alcan V., Uçar M., 2019. Investigation of The Sensitivity Tolerance Parameter to Noise-Related Effect Using Sample Entropy. CISET - 2nd Cilicia International Symposium on Engineering and Technology; 146-149
• Baig M.M., Gholamhosseini H., Connolly M.J., 2013. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults, Medical & Biological Engineering & Computing, 51, 485-495.
• Ferrario M., Signorini M.G., Magenes G., Cerutti S., 2006. Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress, IEEE Transactions on Biomedical Engineering, 53,119-125.
• Guo H.W., Huang Y.S., Chien J.C., Shieh J.S., 2015. Short-term analysis of heart rate variability for emotion recognition via a wearable ECG device, IEEE International Conference on Intelligent Informatics and Biomedical Sciences 262-265.
• Kaya H., Uçar E., Alcan V.,2019. A Multiscale Entropy Based Approach For Analysis Of Surface EMG Signals. 2nd Cilicia International Symposium on Engineering and Technology,306-310
• Kleiger R.E., Stein P.K., Bigger J.T., 2005. Jr. Heart rate variability: measurement and clinical utility, Annual Noninvasive Electrocardiology, 10,88-101.
• Kuntzelman K., Jack Rhodes L., Harrington L. N., Miskovic V., 2018. A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data, Brain and Cognition, 123, 126-135.
• Lake D.E., Richman J.S., Griffin M.P., Moorman J.R., 2002. Sample entropy analysis of neonatal heart rate variability, American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283, 789-797.
• Malik M., Bigger T., Camm A.J., Kleiger R.E., Malliani A., Moss A.J., et al.,1996. Heart rate variability: standards of measurement, physiological interpretation and clinical use, European Heart Journal,17, 354-814.
• Pincus S.M., 1991. Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Sciences, 88, 2297-2301.
• Richman J.S., Moorman J.R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. The American Journal of Physiology-Heart and Circulatory Physiology, 278, H2039–H2049.
• Rosenberg M.A., Samuel M., Thosani A., Zimetbaum P.J., 2013. Use of a noninvasive continuous monitoring device in the management of atrial fibrillation: A pilot study, Pacing and Clinical Electrophysiology, 36, 328-333.
• Stein P.K., Reddy A., 2005. Non-linear heart rate variability and risk stratification in cardiovascular disease, Indian Pacing and Electrophysiology Journal, 5, 210-220.
• Villareal R.P., Liu B.C., Massumi A., 2002. Heart rate variability and cardiovascular mortality. Current Atherosclerosis Reports, 4, 120-127.
• Xiong W., Faes L., Ivanov, P.C., 2017. Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: effects of artifacts, nonstationarity, and long-range correlations, Physical Review, E 95, no. 6, 2017.