Robust off-line heartbeat detection using ECG and pressure-signals.

Artefacts in pressure- and ECG-signals generally arise due to different causes. Therefore, the combined analysis of both signals can increase the effectiveness of heartbeat detection compared to analysis using solely ECG-signals. In this paper, we present an algorithm for heartbeat annotation by combining the analysis of both the pressure- and ECG-signals. The novelties of our algorithm are as follows: (1) development of a new approach for annotating heartbeats using pressure-signals, (2) development of a mechanism that identifies and corrects paced rhythms, and (3) development of a noise detection approach. Our algorithm is tested on the datasets from the extended phase of the Physionet CINC-2014 challenge and produces an overall score of 87.31%. Finally, we put forth several recommendations that could further improve our algorithm.

[1]  James McNames,et al.  An automatic beat detection algorithm for pressure signals , 2005, IEEE Transactions on Biomedical Engineering.

[2]  Bo Yang,et al.  Robust identification of heartbeats with blood pressure signals and noise detection , 2014, Computing in Cardiology 2014.

[3]  F. De Boer,et al.  A Robust QRS Complex Detection Algorithm Using Dynamic Thresholds , 2008, International Symposium on Computer Science and its Applications.

[4]  R. Poli,et al.  Genetic design of optimum linear and nonlinear QRS detectors , 1995, IEEE Transactions on Biomedical Engineering.

[5]  Samit Ari,et al.  On an algorithm for detection of QRS complexes in noisy electrocardiogram signal , 2011, 2011 Annual IEEE India Conference.

[6]  K. J. Ray Liu,et al.  A robust method for QRS detection based on modified p-spectrum , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[8]  Hlaing Minn,et al.  A Patient-Adaptive Profiling Scheme for ECG Beat Classification , 2010, IEEE Transactions on Information Technology in Biomedicine.

[9]  Ivan A Dotsinsky,et al.  “Open” , 2019, European Romantic Review.

[10]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[11]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[12]  José Carlos Teixeira de Barros Moraes,et al.  A QRS complex detection algorithm using electrocardiogram leads , 2002, Computers in Cardiology.

[13]  François Portet,et al.  Evaluation of real-time QRS detection algorithms in variable contexts , 2005, Medical and Biological Engineering and Computing.

[14]  John R. Hampton,et al.  The ECG Made Easy , 1973 .

[15]  Ivaylo I Christov,et al.  Real time electrocardiogram QRS detection using combined adaptive threshold , 2004, Biomedical engineering online.

[16]  M B Shamsollahi,et al.  A model-based Bayesian framework for ECG beat segmentation , 2009, Physiological measurement.