Respiratory Analysis with Electrocardiogram Data: Evaluation of Pan-Tompkins Algorithm and Cubic Curve Interpolation Method

Year 2024, , 374 - 383, 15.05.2024

Mert Süleyman Demirsoy , Ayşe Nur Ay Gül

https://doi.org/10.34248/bsengineering.1418802

Cited By: 1

Abstract

Advancements in bioinstrumentation have facilitated the easier monitoring of biometric signals such as electrocardiogram (ECG) and respiration. This development is particularly crucial for the diagnosis and management of various conditions like stress and sleep disorders. Two commonly used features in heart rate variability (HRV) analysis derived from ECG data are standard deviation and serial correlation coefficients of R-R intervals (the time durations between heartbeats). The former utilizes the fundamental components of QRS complexes, while the latter is designed to extract relationships between respiration and heart rate. In the proposed methodology, R-R wave detection is performed on processed ECG data using the Pan-Tompkins algorithm, and the respiration duration for each R-R interval from respiration data is selected. Additionally, missing respiration data for selected R-R intervals is interpolated based on the interpolation method. The results of this study are compared with the standard interpolation and cubic spline interpolation models to assess the effectiveness of the proposed method and its ability to capture temporal fluctuations. Since standard interpolation fails to accurately detect respiration data from R-R intervals and cannot precisely handle missing R-R intervals in short samples, cubic spline interpolation is recommended as a replacement and its results are presented. The obtained results provide insights into the effectiveness and application of the Pan-Tompkins algorithm, FFT (Fast fourier transform) implementation, and cubic spline interpolation in the selection of respiration and R-wave features. According to the findings of the study, in the analysis conducted on 2-second samples with a 1000 Hz sampling frequency created from each participant's respiratory data set, missing respiratory data were successfully reconstructed from the R-R intervals of the ECG data using standard and cubic curve interpolation methods. Upon examination of RMSE (Root mean square error) values, it was observed that for 30% of the participants, as RMSE values increased, completion counts for standard interpolation increased, while completion counts for cubic curve interpolation decreased. Conversely, when RMSE values decreased, 60% of the participants showed a decrease in completion counts for standard interpolation and an increase in completion counts for cubic curve interpolation. A 10% participant group was identified where there was no apparent relationship between RMSE values and interpolation method. This indicates that in 90% of the participants, there is a linear relationship between the study's interpolation method, RMSE values, and completion counts for missing R-R intervals.

Keywords

Electrocardiogram, Respiration, Pan-Tompkins algorithm, Curve interpolation

References

• Akshay N, Jonnabhotla NAV, Sadam N, Yeddanapudi ND. 2010. ECG noise removal and QRS complex detection using UWT. International Conference on Electronics and Information Engineering, 2: 438.
• Apandi ZFM, Ikeura R, Hayakawa S. 2018. Arrhythmia detection using MIT-BIH dataset: A review. International Conference on Computational Approach in Smart Systems Design and Applications ICASSDA, August 15-17, Serawak, Malaysia, pp: 1-5. IEEE.
• Ay AN, Yıldız MZ, Boru B. 2017. Real-time feature extraction of ECG signals using NI LabVIEW. Sakarya Univ J Sci, 21(4): 576-583.
• Ay AN, Yildiz MZ. 2021. The effect of attentional focusing strategies on EMG-based classification. Biomed Eng, 66(2): 153-158.
• Ay AN, Yildiz MZ. 2023. The performance of an electromyography‐based deep neural network classifier for external and internal focus instructions. Concurr Comput: Pract Exper, 35(2): e7470.
• Bach DR, Staib M. 2015. A matching pursuit algorithm for inferring tonic sympathetic arousal from spontaneous skin conductance fluctuations. Psychophysiology, 52(8): 1106-1112.
• Benosman MM, Bereksi-Reguig F, Salerud, EG. 2017. Strong real-time QRS complex detection. J Mech Medic Biol, 17(08): 1750111.
• Berntson GG, Cacioppo JT, Quigley KS. 1993. Respiratory sinus arrhythmia: Autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology, 30(2): 183-196.
• Borges G, Brusamarello V. 2016. Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit Comput, 30: 859-867.
• Chang Q, Liu R, Shen Z. 2013. Effects of slow breathing rate on blood pressure and heart rate variabilities. Int J Cardiol, 169(1): e6-e8.
• Cheadle JE, Goosby BJ, Jochman JC, Tomaso CC, Kozikowski Yancey CB, Nelson TD. 2020. Race and ethnic variation in college students’ allostatic regulation of racism-related stress. Proc National Acad Sci, 117(49): 31053-31062.
• Chester JG, Rudolph JL. 2011. Vital signs in older patients: age-related changes. J American Medic Direct Assoc, 12(5): 337-343.
• Choi A, Shin H. 2018. Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Front Physiol, 9: 922.
• Dias D, Paulo Silva Cunha J. 2018. Wearable health devices-vital sign monitoring, systems and technologies. Sensors, 18(8): 2414.
• Hamida El Naser Y, Karayel D. 2023. Modeling the effects of external oscillations on mucus clearance in obstructed airways. Biomechan Model Mechanobiol, 2023: 1-14.
• Hao W, Rui D, Song L, Ruixiang Y, Jinhai Z, Juan C. 2021. Data processing method of noise logging based on cubic spline interpolation. Appl Math Nonlinear Sci, 6(1): 93-102.
• Harikumar R, Shivappriya SN. 2011. Analysis of QRS detection algorithm for cardiac abnormalities–A review. Int J Soft Comput Eng, 1(5): 80-88.
• Hayano J, Yuda E. 2019. Pitfalls of assessment of autonomic function by heart rate variability. J Physiol Anthropol, 38(1): 1-8.
• Izumi S, Nakano M, Yamashita K, Nakai Y, Kawaguchi H, Yoshimoto M. 2015. Noise tolerant heart rate extraction algorithm using short-term autocorrelation for wearable healthcare systems. IEICE Transact Info Syst, 98(5): 1095-1103.
• Jelsma EB, Goosby BJ, Cheadle JE. 2021. Do trait psychological characteristics moderate sympathetic arousal to racial discrimination exposure in a natural setting?. Psychophysiology, 58(4): e13763.
• Kohler BU, Hennig C, Orglmeister R. 2002. The principles of software QRS detection. IEEE Eng Medic Biol Magazine, 21(1): 42-57.
• Li Q, Clifford GD. 2008. Suppress false Arrhythmia alarms of ICU monitors using heart rate estimation based on combined arterial blood pressure and ECG analysis. 2nd International Conference on Bioinformatics and Biomedical Engineering, May 16-18, London, UK, pp: 2185-2187.
• Luz EJDS, Schwartz WR, Cámara-Chávez G, Menotti D. 2016. ECG-based heartbeat classification for arrhythmia detection: A survey. Comput Methods Prog Biomed, 127: 144-164.
• Marinho LB, de MM Nascimento N, Souza JWM, Gurgel MV, Rebouças Filho PP, de Albuquerque VHC. 2019. A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Gener Comput Syst, 97: 564-577.
• Rahman H, Ahmed MU, Begum, S, Funk P. 2016. Real time heart rate monitoring from facial RGB color video using webcam. 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), June 2-3, Malmö, Sweden, No: 129.
• Ryan SM, Goldberger AL, Pincus SM, Mietus J, Lipsitz LA. 1994. Gender-and age-related differences in heart rate dynamics: are women more complex than men?. J American College Cardiol, 24(7): 1700-1707.
• Sahoo SK, Subudhi AK, Kanungo B, Sabut SK. 2015. Feature extraction of ECG signal based on wavelet transform for arrhythmia detection. International Conference on Electrical, Electronics, Signals, Communication and Optimization, January 24-25, Andhrapradesh, India, pp: 24-25.
• Shin DI, Song JH, Joo S.K, Huh SJ. 2012. Hybrid vital sensor of health monitoring system for the elderly. Wireless Mobile Communication and Healthcare: Second International 4-4-ICST Conference, MobiHealth 2011, October 5-7, Kos Island, Greece, pp: 329-334.
• Sroufe LA. 1971. Effects of depth and rate of breathing on heart rate and heart rate variability. Psychophysiology, 8(5): 648-655.
• Suzuki T, Ouchi K, Kameyama KI, Takahashi M. 2009. Development of a sleep monitoring system with wearable vital sensor for home use. International Conference on Biomedical Electronics and Devices, January 14-17, Porto, Potugal, pp: 326-331.
• Vijaya G, Kumar V, Verma HK. 1998. ANN-based QRS-complex analysis of ECG. J Medic Eng Technol, 22(4): 160-167.
• Wickramasuriya DS, Faghih RT. 2020. A marked point process filtering approach for tracking sympathetic arousal from skin conductance. IEEE Access, 8: 68499-68513.
• Ye C, Coimbra MT, Kumar BV. 2010. Arrhythmia detection and classification using morphological and dynamic features of ECG signals. Annual International Conference of the IEEE Engineering in Medicine and Biology, August 30 - September 4 Buenos Aires, Brazil, pp: 1918-1921.