A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis
<p>A typical electrocardiography (ECG) waveform and RR interval.</p> "> Figure 2
<p>Block diagram of ECG signal processing.</p> "> Figure 3
<p>Vectorization process.</p> "> Figure 4
<p>Final result of the vectorization and noise-removal processes.</p> "> Figure 5
<p>(<b>a</b>) Relation of the ECG signal sequence to the automata states and other ECG parameters and (<b>b</b>) the automata’s state transition diagram.</p> "> Figure 6
<p>Occurrence of the signal noted as an “isolated QRS-like artefact” in record #16272 [<a href="#B12-algorithms-11-00083" class="html-bibr">12</a>,<a href="#B13-algorithms-11-00083" class="html-bibr">13</a>,<a href="#B15-algorithms-11-00083" class="html-bibr">15</a>].</p> "> Figure 7
<p>(a, b, and c) Typical waveforms, which sometimes occurred and were annotated as beats in the Fantasia database, but cannot be recognized by the automata as heartbeats [<a href="#B12-algorithms-11-00083" class="html-bibr">12</a>,<a href="#B13-algorithms-11-00083" class="html-bibr">13</a>,<a href="#B14-algorithms-11-00083" class="html-bibr">14</a>,<a href="#B15-algorithms-11-00083" class="html-bibr">15</a>].</p> "> Figure 8
<p>A single misdetection of the R peak results in wrong NN intervals, which can greatly modify the result of the frequency analysis of the HRV as compared to the actual analysis without misdetection.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Vectorization Process
2.2. Noise Removal
2.3. Automata Recognizer
2.4. Probability Function
- P = probability function of the next R peak;
- tn = time interval of the next R peak;
- = mean of the RR interval; and
- σ = standard deviation of the RR interval.
2.5. Evaluation Setup
3. Results
4. Discussion
Author Contributions
Acknowledgments
Conflicts of Interest
References
- Robinson, B.F.; Epstein, S.E.; Beiser, G.D.; Braunwald, E. Control of the heart rate by the autonomic nervous system in man on the interrelation between baroreceptor mechanisms & exercise. Circulation 1966, 19, 400–411. [Google Scholar]
- Camm, A.J.; Malik, M.; Bigger, J.T.; Breithardt, G.; Cerutti, S.; Cohen, R.J.; Coumel, P.; Fallen, E.L.; Kennedy, H.L.; Kleiger, R.E.; et al. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Task for of the European society of cardiology and the North American society for pacing and electrophysiology. Circulation 1996, 93, 1043–1065. [Google Scholar]
- Acharya, U.R.; Joseph, K.P.; Kannathal, N.; Lim, C.M.; Suri, J.S. Heart rate variability: A review. Med. Biol. Eng. Comput. 2006, 44, 1031–1051. [Google Scholar] [CrossRef] [PubMed]
- Acharya, R.; Krishnan, S.M.; Spaan, J.A.; Suri, J.S. Advances in Cardiac Signal Processing; Springer: Berlin/Heidelberg, Germany, 2007. [Google Scholar]
- Pang, D.; Igasaki, T.; Maehara, J. Long-term monitoring of heart rate variability toward practical use in intensive/high care unit. In Proceedings of the 9th Biomedical Engineering International Conference (BMEiCON), Luang Prabang, Laos, 7–9 December 2016; pp. 1–6. [Google Scholar]
- Kleiger, R.E.; Stein, P.K.; Bosner, M.S.; Rottman, J.N. Time domain measurements of heart rate variability. Cardiol. Clin. 1992, 10, 487–498. [Google Scholar] [PubMed]
- Pruvot, E.; Thonet, G.; Vesin, J.M.; van-Melle, G.; Seidl, K.; Schmidinger, H.; Brachmann, J.; Jung, W.; Hoffmann, E.; Tavernier, R.; et al. Heart rate dynamics at the onset of ventricular tachyarrhythmias as retrieved from implantable cardioverter-defibrillators in patients with coronary artery disease. Circulation 2000, 101, 2398–2404. [Google Scholar] [CrossRef] [PubMed]
- Lombardi, F.; Porta, A.; Marzegalli, M.; Favale, S.; Santini, M.; Vincenti, A.; De Rosa, A. Implantable Cardioverter Defibrillator-Heart Rate Variability Italian Study Group. Heart rate variability patterns before ventricular tachycardia onset in patients with an implantable cardioverter defibrillator. Participating Investigators of ICD-HRV Italian Study Group. Am. J. Cardiol. 2000, 86, 959–963. [Google Scholar] [PubMed]
- Meyerfeldt, U.; Wessel, N.; Schütt, H.; Selbig, D.; Schumann, A.; Voss, A.; Kurths, J.; Ziehmann, C.; Dietz, R.; Schirdewan, A. Heart rate variability before the onset of ventricular tachycardia: Differences between slow and fast arrhythmias. Int. J. Cardiol. 2002, 84, 141–151. [Google Scholar] [CrossRef]
- Tsuji, H.; Venditti, F.J.; Manders, E.S.; Evans, J.C.; Larson, M.G.; Feldman, C.L.; Levy, D. Determinants of heart rate variability. J. Am. Coll. Cardiol. 1996, 28, 1539–1546. [Google Scholar] [CrossRef]
- Ozdemir, O.; Soylu, M.; Demir, A.D.; Topaloglu, S.; Alyan, O.; Geyik, B.; Kutuk, E. Increased sympathetic overactivity as cause of exercise-induced ventricular tachycardia in patients with normal coronary arteries. Texas Heart Inst. J. 2003, 30, 100–104. [Google Scholar]
- Goldberger, A.L.; Amaral, L.A.N.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 2000, 101, e215–e220. [Google Scholar] [CrossRef] [PubMed]
- PhysioBank ATM. Available online: https://physionet.org/cgi-bin/atm/ATM (accessed on 30 November 2017).
- Iyengar, N.; Peng, C.K.; Morin, R.; Goldberger, A.L.; Lipsitz, L.A. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am. J. Physiol. 1996, 271, 1078–1084. [Google Scholar] [CrossRef] [PubMed]
- PhysioBank Annotations. Available online: https://physionet.org/physiobank/annotations.shtml#noise (accessed on 30 November 2017).
- Sharma, L.D.; Sunkaria, R.K. A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 2016, 87, 194–204. [Google Scholar] [CrossRef]
- Elgendi, M. Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE 2014, 9, e84018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pan, J.; Tompkins, W. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 1985, 32, 230–236. [Google Scholar] [CrossRef] [PubMed]
Symbol | Slope Range |
---|---|
Slope11 | slope ≥ 0.007Vpp |
Slope10 | 0.007Vpp > slope ≥ 0.0056Vpp |
Slope09 | 0.0056Vpp > slope ≥ 0.0042Vpp |
Slope08 | 0.0042Vpp > slope ≥ 0.0021Vpp |
Slope07 | 0.0021Vpp > slope ≥ 0.0007Vpp |
Slope06 | 0.0007Vpp > slope ≥ −0.0009Vpp |
Slope05 | −0.0009Vpp > slope ≥ −0.0027Vpp |
Slope04 | −0.0027Vpp > slope ≥ −0.0054Vpp |
Slope03 | −0.0054Vpp > slope ≥ −0.0072Vpp |
Slope02 | −0.0072Vpp > slope ≥ −0.009Vpp |
Slope01 | slope < −0.009Vpp |
Symbol | Function |
---|---|
a | |
b | |
c | |
d | |
e | |
f | |
g | |
h | |
i | |
j |
Record # | Total Beats | Se (%) | PPR (%) | DER (%) | Ac (%) | Processing Time (ms) |
---|---|---|---|---|---|---|
16265 | 14,809 | 100 | 100 | 0 | 100 | 310 |
16272 | 10,485 | 100 | 100 | 0 | 100 | 413 |
16273 | 13,629 | 100 | 100 | 0 | 100 | 269 |
16420 | 13,555 | 100 | 100 | 0 | 100 | 253 |
16483 | 15,638 | 99.994 | 99.994 | 0.013 | 99.987 | 407 |
16539 | 11,952 | 100 | 100 | 0 | 100 | 383 |
16773 | 12,464 | 99.984 | 100 | 0.016 | 99.984 | 331 |
16786 | 12,222 | 100 | 100 | 0 | 100 | 296 |
16795 | 13,819 | 100 | 100 | 0 | 100 | 501 |
17052 | 11,496 | 100 | 100 | 0 | 100 | 338 |
17453 | 14,463 | 100 | 100 | 0 | 100 | 283 |
18177 | 14,900 | 100 | 100 | 0 | 100 | 510 |
18184 | 13,876 | 100 | 100 | 0 | 100 | 377 |
19088 | 15,332 | 100 | 100 | 0 | 100 | 527 |
19090 | 13,350 | 100 | 100 | 0 | 100 | 417 |
19093 | 11,663 | 100 | 100 | 0 | 100 | 286 |
19140 | 14,436 | 100 | 100 | 0 | 100 | 480 |
19830 | 18,925 | 99.903 | 99.995 | 0.103 | 99.898 | 47 |
Mean | 13,272 | 99.993 | 99.999 | 0.007 | 99.993 | 385 |
Record # | Total Beats | Se (%) | PPR (%) | DER (%) | Ac (%) | Processing Time (ms) |
---|---|---|---|---|---|---|
f1o01 | 7169 | 100 | 100 | 0 | 100 | 386 |
f1o02 | 6823 | 100 | 100 | 0 | 100 | 492 |
f1o03 | 7728 | 100 | 100 | 0 | 100 | 545 |
f1o04 | 6230 | 99.952 | 99.968 | 0.080 | 99.920 | 253 |
f1o05 | 5730 | 100 | 100 | 0 | 100 | 341 |
f1o06 | 6231 | 99.968 | 100 | 0.032 | 99.968 | 377 |
f1o08 | 8488 | 99.976 | 99.976 | 0.047 | 99.953 | 325 |
f1o09 | 4925 | 99.959 | 100 | 0.041 | 99.959 | 391 |
f1o10 | 8241 | 100 | 100 | 0 | 100 | 369 |
f1y01 | 8709 | 100 | 100 | 0 | 100 | 549 |
f1y02 | 7035 | 100 | 100 | 0 | 100 | 493 |
f1y03 | 7643 | 100 | 100 | 0 | 100 | 514 |
f1y04 | 5511 | 100 | 100 | 0 | 100 | 248 |
f1y05 | 6965 | 99.986 | 99.986 | 0.029 | 99.971 | 272 |
f1y06 | 7086 | 99.958 | 99.972 | 0.071 | 99.930 | 262 |
f1y07 | 5947 | 100 | 99.983 | 0.017 | 99.983 | 247 |
f1y08 | 7289 | 100 | 100 | 0 | 100 | 541 |
f1y09 | 8021 | 100 | 100 | 0 | 100 | 522 |
f1y10 | 8693 | 100 | 100 | 0 | 100 | 412 |
f2o01 | 7234 | 99.986 | 100 | 0.014 | 99.986 | 543 |
f2o02 | 6372 | 99.984 | 99.953 | 0.063 | 99.937 | 548 |
f2o03 | 6541 | 100 | 100 | 0 | 100 | 274 |
f2o04 | 6902 | 100 | 100 | 0 | 100 | 441 |
f2o06 | 5249 | 100 | 100 | 0 | 100 | 264 |
f2o07 | 5891 | 100 | 100 | 0 | 100 | 202 |
f2o09 | 6072 | 99.951 | 99.967 | 0.082 | 99.918 | 276 |
f2y01 | 8042 | 99.923 | 100 | 0.037 | 99.963 | 333 |
f2y02 | 6574 | 100 | 100 | 0 | 100 | 215 |
f2y03 | 6807 | 100 | 100 | 0 | 100 | 230 |
f2y04 | 8603 | 99.977 | 99.988 | 0.035 | 99.965 | 540 |
f2y05 | 9244 | 99.838 | 99.989 | 0.173 | 99.827 | 278 |
f2y06 | 6851 | 100 | 100 | 0 | 100 | 167 |
f2y07 | 6506 | 100 | 100 | 0 | 100 | 214 |
f2y08 | 7249 | 99.724 | 99.917 | 0.360 | 99.642 | 368 |
f2y10 | 7113 | 99.859 | 99.972 | 0.169 | 99.831 | 240 |
Mean | 13,272 | 99.957 | 99.991 | 0.053 | 99.947 | 362 |
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Pang, D.; Igasaki, T. A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis. Algorithms 2018, 11, 83. https://doi.org/10.3390/a11060083
Pang D, Igasaki T. A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis. Algorithms. 2018; 11(6):83. https://doi.org/10.3390/a11060083
Chicago/Turabian StylePang, David, and Tomohiko Igasaki. 2018. "A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis" Algorithms 11, no. 6: 83. https://doi.org/10.3390/a11060083