Deterioration of R-Wave Detection in Pathology and Noise: A Comprehensive Analysis Using Simultaneous Truth and Performance Level Estimation

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 9; pp. 2163 - 2175
• Main Authors: Kashif, Muhammad, Jonas, Stephan M., Deserno, Thomas M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arrhythmias, Cardiac - diagnosis; Band-pass filters; Data processing; Diagnosis, Computer-Assisted - methods; EKG; Electrocardiography; Electrocardiography (ECG); Electrocardiography, Ambulatory - methods; Feature extraction; Ground truth; Humans; Image processing; multimorbid subjects; Noise measurement; Pattern Recognition, Automated - methods; R-wave detection; Reproducibility of Results; Sensitivity and Specificity; Signal analysis; Signal-To-Noise Ratio; simultaneous truth and performance level estimation (STAPLE); Transforms
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2016.2633277

Objective: For long-term electrocardiography (ECG) recordings, accurate R-wave detection is essential. Several algorithms have been proposed but not yet compared on large, noisy, or pathological data, since manual ground-truth establishment is impossible on such large data. Methods: We apply the simultaneous truth and performance level estimation (STAPLE) method to ECG signals comparing nine R-wave detectors: Pan and Tompkins (1985), Chernenko (2007), Arzeno et al. (2008), Manikandan et al. (2012), Lentini et al. (2013), Sartor et al. (2014), Liu et al. (2014), Arteaga-Falconi et al. (2015), and Khamis et al. (2016). Experiments are performed on the MIT-BIH database, TELE database, PTB database, and 24/7 Holter recordings of 60 multimorbid subjects. Results: Existing approaches on R-wave detection perform excellently on healthy subjects (F-measure above 99% for most methods), but performance drops to a range of F = 90.10% (Khamis et al.) to F = 30.10% (Chernenko) when analyzing the 37 million R-waves of multimorbid subjects. STAPLE improves existing approaches (ΔF = 0.04 for the MIT-BIH database and ΔF = 0.95 for the TELE database) and yields a relative (not absolute) scale to compare algorithms' performances. Conclusion: More robust R-wave detection methods or flexible combinations are required to analyze continuous data captured from pathological subjects or that is recorded with dropouts and noise. Significance: STAPLE algorithm has been adopted from image to signal analysis to compare algorithms on large, incomplete, and noisy data without manual ground truth. Existing approaches on R-wave detection weakly perform on such data.