Kalp Hızı Değişkenliği Zaman Alanı Ölçümleri Kullanarak PAF Atağının Tahmini

Yıl 2018, Cilt: 8 Sayı: 2, 536 - 542, 01.06.2018

Ali Narin Yalçın İşler Mahmut Özer

Öz

Paroksismal atriyal fibrilasyon PAF , çoğunlukla rastlanan aritmi türüdür ve PAF atağı başlamadan önce öngörmek için geçerli bir yöntem yoktur. Bu çalışmada, k- en yakın komşu k-nn sınıflandırıcı algoritmasıyla kalp hızı değişkenliği KHD zaman alanı ölçümleri kullanılarak PAF olayının gerçekleşmeden önce tahmin edilmesi amaçlanmıştır. 49 normal, 25 PAF hastası olup atak geçirmeyen ve 25 PAF hastası olup verinin bitiminde atak geçiren 5 dakikalık veriler üzerinden geleneksel zaman alanı ölçümleri elde edilmiştir. Tüm bu ölçümlerin istatistiksel anlamlılık değerleri araştırılmıştır. İstatistiksel anlamlı olan öznitelikler kullanılarak k değerinin 1 ila 19 arasındaki tek değerleri için k-nn sınıflandırıcı algoritmasıyla sınıflandırılmıştır. Bu işlem hemen PAF atağı geçiren verilerin, normal ve atak geçirmeyen verilerin kontrol grubunda olduğu çalışma 1 ve hemen atak geçiren verilen, hemen atak geçirmeyen verilerin kontrol grubunda olduğu çalışma 2 için ayrı ayrı çalıştırılmıştır. Sonuç olarak, SDNN, RMSSD ve pNN50 ölçümlerinin istatistiksel anlamlılık değerlerinin 0-5 dakika, 2,5-7,5 dakika ve 5-10 dakika aralıklarında p

Anahtar Kelimeler

Kalp hızı değişkenliği, K- en yakın komşu, Paroksismal atrial fibrilasyon, Tahmin

Prediction of PAF Attacks using Time-Domain Measures of Heart Rate Variability

Öz

Paroxysmal atrial fibrillation PAF is the mainly encountered type of arrhythmia and there is no validated method to predict a PAF attack before it occurs. In this study, predicting the PAF event was aimed using time-domain heart rate variability HRV measures in k- nearest neighbor k-nn classifier. Traditional time-domain HRV measures were analyzed in every 5-minute segments from 49 normal subjects, 25 patients with PAF attack and 25 patients with no attack within 45 minutes. All features were investigated whether they showed statistically significance. Significant features were classified by k-nn for odd numbers of neighbors between 1 and 19. This setup was run with two different configurations as study 1 to discriminate patients with PAF attack from normals and patients with no attack, and study 2 to discriminate patients with PAF attack from patients with no attack. SDNN, RMSSD and pNN50 measures were found to show statistically significant differences with p less than 0.05 in segments of 0-5 min, 2.5-7.5 min and 5-10 min intervals only. The maximum classification accuracy was obtained in the time interval of 2.5-7.5 minutes with %79 for Study 1 and just before attack with %80 for Study 2 in the time interval of 0-5 minutes. Results showed that the prediction of PAF events was possible when the classification between normal subjects from PAF patients was accurate. PAF attack can be determined 2.5 minutes earlier by simple classifier algorithms.

Anahtar Kelimeler

Heart Rate Variability, Paroxysmal Atrial Fibrillation, Prediction, K-Nearest Neighbor

Kaynakça

• Alcaraz, R., Arturo, M., Jose, JR. 2015. Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation. Comput. Meth. Prog. Bio., 119(2): 110-119.
• Ashcroft, S., Pereira, C. 2003. Practical statistics for the biological sciences: simple pathways to statistical analyses. Palgrave Macmillan, 1st edt.
• Boon, KH., Khalil-Hani, M., Malarvili, MB., Sia, CW. 2016. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences. Comput. Meth. Prog. Bio., 134: 187-196
• Camm AJ., Kirchhof P., Lip GY., Schotten U., Savelieva I., Ernst S., Van Gelder IC., Al-Attar N., Hindricks G., Prendergast B., Heidbuchel H., Alfieri O., Angelini A., Atar D., Colonna P., De Caterina R., De Sutter J., Goette A., Gorenek B., Heldal M., Hohloser SH., Kolh P., Le Heuzey JY., Ponikowski P., Rutten FH. 2010. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur. Heart J., 31(19): 2369-2429.
• Camm AJ., Malik M., Bigger JT., Breithardt G., Cerutti S., Cohen RJ., Coumel P., Fallen EL., Kennedy HL., Kleiger RE., Lombardi F., Malliani A, Moss AJ., Rottman JN., Schmidt G., Schwartz PJ., Singer DH. 1996. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur. Heart J., 17: 354-381.
• Chazal, P., Heneghan, C. 2001. Automated assessment of atrial fibrillation. Comput. Cardiol., 28: 117-120.
• Chesnokov, YV. 2008. Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif. Intell. Med., 43(2): 151-165.
• Duda, RO., Hart, PE., Stork, DG. 2001. Pattern classification. New York: John Wiley and Sons, 2nd edt. Hickey, B., Heneghan, C. 2002. Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures. Comput. Cardiol., 217-220.
• Isler, Y., Kuntalp, M. 2007. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Comput. Biol. Med., 37(10): 1502-1510.
• Isler, Y., Narin, A., Ozer, M. 2015. Comparison of the effects of cross-validation methods on determining performances of classifiers used in diagnosing congestive heart failure. Meas. Sci. Rev., 15(4): 196-201.
• Isler, Y., Narin, A., Ozer, M., Perc M. 2019. Multi-stage classification of congestive heart failure based on short-term heart rate variability. Chaos Soliton Fract., 118: 145-151.
• January, CT., Wann, LS., Alpert, JS., Calkins, H., Cigarroa, JE., Cleveland, JC., ..., Murray, KT. 2014. 2014 AHA/ACC/ HRS guideline for the management of patients with atrial fibrillation: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J. Am. Coll. Cardiol., 64(21): 2246-2280.
• Krstacic, G., Garnberger, D., Smuc, T., Krstacic, A. 2001. Some important R-R interval based paroxysmal atrial fibrillation predictors. Comput. Cardiol., 28: 409-412.
• Langley, P., Di Bernardo, D., Allen, J., Bowers, E., Smith, FE., Vecchietti, S., Murray, A. 2001. Can paroxysmal atrial fibrillation be predicted?. Comput. Cardiol., 28: 121-124.
• Lynn, KS., Chiang, HD. 2001. A two-stage solution algorithm for paroxysmal atrial fibrillation prediction, Comput. Cardiol., 28: 405-407. khaus, H. 2001. Screening and prediction of paroxysmal atrial fibrillation by analysis of heart rate variability parameters. Comput. Cardiol., 28: 129-132.
• Mohebbi, M., Ghassemian, H. 2012. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput. Meth. Prog. Bio., 105: 40-49.
• Moody, G., Goldberger, A., McClennen, S., Swiryn, S. 2001. Predicting the onset of paroxysmal atrial fibrillation. Comput. Cardiol., 28: 113-116.
• Narin, A., Isler, Y., Ozer, M., Perc M. 2018. Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability. Physica A Stat. Mech. Appl., 509: 56-65.
• Narin, A., Ozer M., Isler, Y. 2017. Effect of linear and nonlinear measurements of heart rate variability in prediction of PAF attack. IEEE 25th Signal Processing and Communications Applications Conference (SIU), 1-4. Antalya.
• Narin, A., Isler, Y., Ozer, M. 2016. Early prediction of paroxysmal atrial fibrillation using frequency domain measures of heart rate variability. IEEE In Medical Technologies National Congress (TIPTEKNO’2016), 1-4, Antalya.
• Narin, A., Isler, Y., Ozer, M. 2014. Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. Comput. Biol. Med., 45: 72-79.
• Park, J., Sangwook, L., Moongu, J. 2009. Atrial fibrillation detection by heart rate variability in Poincare plot. Biomed. Eng. Online, 38: 1-12.
• Schrier, G., Kastner, P., Marko, W. 2001. An automatic ECG processing algorithm to identify patients prone to paroxismal atrial fibrillation. Comput. Cardiol., 28: 133-135.
• Segerson, NM., Smith, ML., Wasmund, SL., Lux, RL., Daccarett, M., Hamdan, MH. 2008. Heart rate variability measures during sinus rhythm predict cycle length entropy during atrial fibrillation. J. Cardiovasc. Electr., 19(10): 1031- 1036.
• Shin, DG., Yoo, CS., Yi, SH., Bae, JH., Kim, YJ., Park, JS., Hong, GR. 2006. Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the RR interval dynamics before the spontaneous onset of atrial fibrillation. Circ. J., 70(1): 94- 99.
• Thuraisingham, RA. 2016. Development of an alert system for subjects with paroxysmal atrial fibrillation. J. Arrhythm., 32: 57-61.
• Uyarel, H., Onat, A., Yüksel, H., Can, G., Ordu, S., Dursunoğlu, D. 2008. Incidence, prevalence, and mortality estimates for chronic atrial fibrillation in Turkish adults. Arch. Turk. Soc. Cardiol, 36(4): 214-222.
• Vikman, S., Mäkikallio, TH., Yli-Mäyry, S., Pikkujämsä, S., Koivisto, AM., Reinikainen, P., ..., Huikuri, HV. 1999. Altered complexity and correlation properties of RR interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circ. J., 100(20): 2079-2084
• Vincenti, A., Brambilla, R., Fumagalli, MG., Merola, R., Pedretti, S. 2006. Onset mechanism of paroxysmal atrial fibrillation detected by ambulatory Holter monitoring. Europace, 8(3): 204-210.
• Yang, ACC., Yin, HW. 2001. Prediction of paroxysmal atrial fibrillation by footprint analysis. Comput. Cardiol., 28: 401- 404.
• Zong, W., Mukkamala, R., Mark, RG. 2001. A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis. Comput. Cardiol., 28: 125-128.