Adaptive thresholding based low complexity QRS detection algorithm

Year 2023, Volume: 25 Issue: 1, 78 - 89, 16.01.2023

Ertuğrul Karakulak

https://doi.org/10.25092/baunfbed.1075661

Cited By: 1

Abstract

In this study, a QRS detection algorithm with a low processing load based on time-domain thresholding is proposed. The ECG signal is filtered only with a low pass filter to reduce the computational load. After the filtering, derivation and squaring are also performed. In the Thresholding stage, a linear decreasing threshold voltage method using addition operation instead of multiplication is proposed. Simulations on MIT-BIT Arrhythmia Database have yielded 99.2925% sensitivity (% Se) and 99.6759% positive predictivity (+ P). The proposed algorithm is compared with two similar algorithms in terms of both performance and processing load. It is shown that the proposed algorithm is better than its counterparts, especially in terms of processing load. However, it is observed that it gave worse results in terms of Sensitivity (% Se).

Keywords

Ecg signal, qrs detection, time domain thresholding, computational load.

Adaptif eşik temelli az karmaşık QRS algılama algoritması

Abstract

Bu çalışmada düşük işlem yoğunluğa sahip, zaman domeninde, adaptif eşik gerilimi tabanlı bir QRS algılama algoritması önerilmiştir. İşlem yükünü azaltmak için EKG işareti sadece alçak geçiren bir filtre ile filtrelenmiştir. Filtrelemenin ardından türev ve kare alma işlemleri sırasıyla yapılmıştır. Adaptif Eşik geriliminin hesaplanmasında çarpma işlem sayısını azaltmak için doğrusal azalan bir eşik gerilimi yöntemi kullanılmış ve önerilmiştir. MIT-BIT Arrhythmia veri tabanından alınan işaretler kullanılarak yapılan simülasyon sonuçlarına göre Sensitivity (% Se) %99.2925 ve Positive Predictivity (+ P) %99.6759 olarak tespit edilmiştir. Önerilen algoritma hem işlem yükü hem de performans açısından benzer iki algoritma ile karşılaştırılmıştır. Önerilen algoritmanın özellikle işlem yükü bakımında benzerlerinden üstün olduğu ancak Sensitivity (% Se) açısından daha kötü sonuçlar verdiği görülmüştür

Keywords

Ekg sinyali, qrs algılama, zaman domeninde eşikleme, işlemsel yük.

References

• Dilaveris P., Gialafos E., Sideris S., Theopistou A., Andrikopoulos GK. Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am Heart J, 135, 5, 733–738, (1998).
• Zywietz, Chr. A brief history of electrocardiography-Progress through technology, Hannover: Biosigna Institute for Biosignal Processing and Systems Research, (2003).
• Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H., Koo, B. H., Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry investigation, 15, 3, 235, (2018).
• Oweis, R. J., Basim O. A-T., QRS detection and heart rate variability analysis: A survey. Biomedical science and engineering, 2, 1, 13-34, (2014).
• Chatterjee, S., Thakur, R. S., Yadav, R. N., Gupta, L., Raghuvanshi, D. K., Review of noise removal techniques in ECG signals. IET Signal Processing, 14, 9, 569-590, (2020).
• Raj, S., Ray, K. C., Shankar, O., Development of robust, fast and efficient QRS complex detector: a methodological review. Australasian physical & engineering sciences in medicine, 41, 3, 581-600, (2018).
• Pan, J., Tompkins WJ., A real-time QRS detection algorithm, IEEE transactions on biomedical engineering. 3, 230-236, (1985).
• Tekeste, T., Saleh, H., Mohammad, B., Ismail, M., Ultra-low power QRS detection and ECG compression architecture for IoT healthcare devices. IEEE Transactions on Circuits and Systems I: Regular Papers, 66, 2, 669-679, (2018).
• Gutiérrez-Rivas, R., Garcia, J. J., Marnane, W. P., Hernández, A. Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sensors Journal, 15, 10, 6036-6043, (2015).
• Engelse, W. A. H., Zeelenberg, C., A single scan algorithm for QRS-detection and feature extraction, Computers in cardiology, 6, 1979, 37-42, (1979).
• Afonso V., Tompkins WJ., Nguyen T., Luo S., ECG beat detection using filter banks, IEEE Trans Biomed Eng, 46, 2, 192–202, (1999).
• Dinh, H. A. N., Kumar, D. K., Pah, N. D., & Burton, P., Wavelets for QRS detection, Australasian Physics & Engineering Sciences in Medicine, 24, 4, 207-211, (2001).
• Szilagyi, L., Wavelet-transform-based QRS complex detection in on-line Holter systems. In Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society, 1, 277, (1999).
• Shyu L-Y, Wu Y-H, Hu W., Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Trans Biomed Eng, 51, 7, 1269–1273, (2004).
• Benitez, D. S., Gaydecki, P. A., Zaidi, A., Fitzpatrick, A. P., A new QRS detection algorithm based on the Hilbert transform. In Computers in Cardiology, 27, 379-382, (2000).
• Physionet Database (2021, 12 January) retrieved from, https://archive.physionet.org/cgi-bin/atm/ATM
• Balda R, Diller G, Deardorff E, Doue J, Hsieh P., The HP ECG analysis program. In: van Bemmel JH, Willems JL (eds) Trends in computer-processed electrocardiograms. North-HollandPublishing, Amsterdam, 197–205, (1977).
• Ahlstrom ML, Tompkins WJ., Automated high-speed analysis of Holter tapes with microcomputers. IEEE Trans Biomed Eng, 30, 10, 651–657, (1983).
• Menrad A., Dual microprocessor system for cardiovascular data acquisition, processing and recording. In: Proceedings of the 1981 IEEE international conference on industrial electronics, control and instrumentation, 64–69, (1981).
• Holsinger WP, Kempner KM, Miller MH., A QRS preprocessor based on digital differentiation. IEEE Trans Biomed Eng, 18(3):212–217, (1971).
• Okada M., A digital filter for the QRS complex detection, IEEE Trans Biomed Eng, 26, 12, 700–703, (1979).
• Sufi, F., Fang, Q., Cosic, I. ECG RR peak detection on mobile phones. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3697-3700, (2007).
• Christov, I. I., Real time electrocardiogram QRS detection using combined adaptive threshold. Biomedical engineering online, 3, 1, 1-9, (2004).