Reliable P wave detection in pathological ECG signals

. 2022 Apr 21 ; 12 (1) : 6589. [epub] 20220421

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články, Research Support, U.S. Gov't, Non-P.H.S.

Perzistentní odkaz   https://www.medvik.cz/link/pmid35449228
Odkazy

PubMed 35449228
PubMed Central PMC9023481
DOI 10.1038/s41598-022-10656-4
PII: 10.1038/s41598-022-10656-4
Knihovny.cz E-zdroje

Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients' treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Software tools used in medical practice usually fail to detect P waves under pathological conditions. Most of recently published approaches have not been tested on such the signals at all. Here we introduce a novel method for accurate and reliable P wave detection, which is success in both normal and pathological cases. Our method uses phasor transform of ECG and innovative decision rules in order to improve P waves detection in pathological signals. The rules are based on a deep knowledge of heart manifestation during various arrhythmias, such as atrial fibrillation, premature ventricular contraction, etc. By involving the rules into the decision process, we are able to find the P wave in the correct location or, alternatively, not to search for it at all. In contrast to another studies, we use three, highly variable annotated ECG databases, which contain both normal and pathological records, to objectively validate our algorithm. The results for physiological records are Se = 98.56% and PP = 99.82% for MIT-BIH Arrhythmia Database (MITDP, with MITDB P-Wave Annotations) and Se = 99.23% and PP = 99.12% for QT database. These results are comparable with other published methods. For pathological signals, the proposed method reaches Se = 96.40% and PP = 91.56% for MITDB and Se = 93.07% and PP = 88.60% for Brno University of Technology ECG Signal Database with Annotations of P wave (BUT PDB). In these signals, the proposed detector greatly outperforms other methods and, thus, represents a huge step towards effective use of fully automated ECG analysis in a real medical practice.

Zobrazit více v PubMed

Thomas, H. et al. Global Atlas of Cardiovascular Disease 2000-2016. Global Heart 13, (2018). PubMed

Kusumoto FM. ECG Interpretation: From Pathophysiology to Clinical Application. New York: Springer; 2009.

Portet F, et al. P wave detector with PP rhythm tracking: Evaluation in different arrhythmia contexts. Physiol. Meas. 2008;29:141–155. doi: 10.1088/0967-3334/29/1/010. PubMed DOI

Cardio Day Holter ECG. GE HealthCare. https://www.gehealthcare.co.uk/en-gb/products/diagnostic-cardiology/ambulatory-ecg (2018).

EKG Holter Cardio Track. SEIVA: Cardiology manufacture http://www.seiva.cz/products/holter-ekg/ (2018).

Biomedical Systems Century C3000 Holter System Specifications. METEC: Marketing of speciality products for cardiology laboratories and hospital wards in Denmark and Sweden http://www.metec.dk/biomedsys/specs_C3000.html (2018).

Cardio Visions Professional 24 hour Holter ECG Software for CardioMera. Meditech: 24-hour Ambulatory Blood Pressure Monitors & Holter ECG Devices http://www.meditech.hu/24-hour-holter-ecg-software-cardiomera.html (2018).

Holter ECG. AMEDTEC—your partner in function diagnosis http://www.amedtec.de/downloads/Holter%20ECG.pdf (2018).

Kusumoto, F. ECG Interpretation (2020). 10.1007/978-3-030-40341-6

Fisch C. Centennial of the string galvanometer and the electrocardiogram. J. Am. Coll. Cardiol. 2000;36:1737–1745. doi: 10.1016/S0735-1097(00)00976-1. PubMed DOI

Goldman, M. Principles of Clinical Electrocardiography (Lange Medical Pubns, 1986).

Elgendi, M., Jonkman, M. & De Boer, F. P wave demarcation in electrocardiogram. In 2009 IEEE 35th Annual Northeast Bioengineering Conference 1–2 (2009).

Lin C, et al. Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter. Signal Process. 2014;104:174–187. doi: 10.1016/j.sigpro.2014.03.011. DOI

Ghaffari A, Homaeinezhad MR, Akraminia M, Atarod M, Daevaeiha M. A robust wavelet-based multi-lead electrocardiogram delineation algorithm. Med. Eng. Phys. 2009;31:1219–1227. doi: 10.1016/j.medengphy.2009.07.017. PubMed DOI

Martinez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng. 2004;51:570–581. doi: 10.1109/TBME.2003.821031. PubMed DOI

Karimipour A, Reza AM. Real-time electrocardiogram P-QRS-T detection—delineation algorithm based on quality—supported analysis of characteristic templates. Comput. Biol. Med. 2014;52:153–165. doi: 10.1016/j.compbiomed.2014.07.002. PubMed DOI

Akhbari, M., Shamsollahi, M.B. & Jutten, Ch. ECG fiducial points extraction by extended Kalman filtering. In Proceedings of the 36th International Conference on Telecommunications and Signal Processing, Vol. 36 628–32 (2013).

Mehta SS, Lingayat NS. Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram: A comparative evaluation. Biomed. Signal Process. Control. 2008;3:341–349. doi: 10.1016/j.bspc.2008.04.002. DOI

Mehta SS, Lingayat NS. Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram: A comparative evaluation. Comput. Methods Programs Biomed. 2009;93:46–60. doi: 10.1016/j.cmpb.2008.07.014. PubMed DOI

Niranjan UM, Murthy ISN. ECG component delineation by Prony's method: A comparative evaluation. Signal Process. 1993;31:191–202. doi: 10.1016/0165-1684(93)90065-I. DOI

Graja S, Boucher JM. Hidden Markov tree model applied to ECG delineation. IEEE Trans. Instrum. Meas. 2005;54:2163–2168. doi: 10.1109/TIM.2005.858568. DOI

Carrault G, Cordier MO, Quiniou R, Wang F. Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms: A comparative evaluation. Artif. Intell. Med. 2003;28:231–263. doi: 10.1016/S0933-3657(03)00066-6. PubMed DOI

Martínez A, Alcaraz R, Rieta JJ. Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiol. Meas. 2011;31:1467–1485. doi: 10.1088/0967-3334/31/11/005. PubMed DOI

Maršánová, L., et al. Automatic Detection of P wave in ECG during ventricular extrasystoles. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering 381–85 (2018).

Maršánová, L., Němcová, A. & Smíšek, R. Detection of P wave during second-degree atrioventricular block in ECG signals. In Proceedings of the 23st Conference STUDENT EEICT 2017 655–659 (2017).

Maršánová, L. Detection of P, QRS and T components of ECG using phasor transform. In Proceedings of the student konference Blansko 2016, 55–58 (2016).

Rao, et al. P and T wave delineation in ECG signals using parametric mixture Gaussian and dynamic programming. Biomed. Signal Process. Control. 2019;51:328–337. doi: 10.1016/j.bspc.2019.03.001. DOI

Friganovic K, Kukolja D, Jovic A, Cifrek M, Krstacic G. Optimizing the Detection of Characteristic Waves in ECG Based on Processing Methods Combinations. IEEE Access. 2018;6:9–26. doi: 10.1109/ACCESS.2018.2869943. DOI

Panigrahy D, Sahu PK. P and T wave detection and delineation of ECG signal using differential evolution (DE) optimization strategy. Australas. Phys. Eng. Sci. Med. 2018;41:225–241. doi: 10.1007/s13246-018-0629-8. PubMed DOI

Laguna P, Jané R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database. Comput. Biomed. Res. 1994;27:45–60. doi: 10.1006/cbmr.1994.1006. PubMed DOI

Maršánová L, Němcová A, Smíšek R, Vítek M, Smital L. Advanced P wave detection in ecg signals during pathology: Evaluation in different arrhythmia contexts. Sci. Rep. 2019;9:19053. doi: 10.1038/s41598-019-55323-3. PubMed DOI PMC

Goldberger AL, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation. 2000;101:215–220. PubMed

Maršánová, L., Nemcova, A., Smisek, R., Smital, L., & Vitek, M. Brno University of Technology ECG signal database with annotations of P wave (BUT PDB). PhysioNet (2020).

Smital L, Vítek M, Kozumplík J, Provazník I. Adaptive wavelet wiener filtering of ECG signals. IEEE Trans. Biomed. Eng. 2013;2:437–445. doi: 10.1109/TBME.2012.2228482. PubMed DOI

Kligfield P, et al. Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology. J. Am. Coll. Cardiol. 2007;49:1109–1127. doi: 10.1016/j.jacc.2007.01.024. PubMed DOI

Kohler BU, Hennig C, Orglmeister R. The principles of software QRS detection. Eng. Med. Biol. Mag. 2002;21:42–57. doi: 10.1109/51.993193. PubMed DOI

Maršánová L, et al. ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study. Sci. Rep. 2017;7:1–11. doi: 10.1038/s41598-017-10942-6. PubMed DOI PMC

Smíšek, R., et al. Cardiac Pathologies detection and classification in 12-lead ECG. In Proceedings of the Computers in Cardiology (2020).

Amar D, Abboud S. P-wave morphology in focal atrial tachycardia using a 3D numerical model of the heart. Int. J. Med. Eng. Inf. 2016;8:263–274.

Maršánová, L, et al. Single-feature method for fast atrial fibrillation detection in ECG signals. In Proceedings of the Computers in Cardiology (2020).

Zhou X, Ding H, Ung B, Pickwell-MacPherson E, Zhang Y. P-wave morphology in focal atrial tachycardia using a 3D numerical model of the heart. Int. J. Med. Eng. Inf. 2016;8:263–274.

Afdala, A., Nuryani, N., Nugroho, A. S., Pickwell-MacPherson, E. & Zhang, Y. Automatic detection of atrial fibrillation using basic Shannon entropy of RR interval feature. J. Phys. Conf. Ser.795 (2017).

Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. 2001;3:45–50. doi: 10.1109/51.932724. PubMed DOI

Elgendi M, Meo M, Abbott D. A proof-of-concept study: Simple and effective detection of P and T waves in arrhythmic ECG signals. Bioengineering. 2016;3:26. doi: 10.3390/bioengineering3040026. PubMed DOI PMC

Němcová A, Smíšek R, Maršánová L, Smital L, Vítek M. A Comparative analysis of methods for evaluation of ECG signal quality after compression. Biomed. Res. Int. 2018;9:1–26. doi: 10.1155/2018/1868519. PubMed DOI PMC

Laguna, P., Mark, R. G., Goldberg, A. & Moody, G. B. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In Proceedings of the Computers in Cardiology 673–676 (1997).

Vítek, M., Hrubeš, J. & Kozumplík, J. A Wavelet-based ECG delineation in multilead ECG Signals: Evaluation on the CSE database. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering 177–180 (2009).

Kumar A, Komaragiri R, Kumar M. From pacemaker to wearable: Techniques for ECG detection systems. J. Med. Syst. 2018;42:34. doi: 10.1007/s10916-017-0886-1. PubMed DOI

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...