Multivariate classification of Brugada syndrome patients based on autonomic response to exercise testing

Ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest and especially during sleep, suggesting that changes in the autonomic modulation may play an important role in arrhythmogenesis. The autonomic response to exercise and subsequent recovery was evaluated on 105 patients diagnosed with BS (twenty-four were symptomatic), by means of a time-frequency heart rate variability (HRV) analysis, so as to propose a novel predictive model capable of distinguishing symptomatic and asymptomatic BS populations. During incremental exercise, symptomatic patients showed higher HFnu values, probably related to an increased parasympathetic modulation, with respect to asymptomatic subjects. In addition, those extracted HRV features best distinguishing between populations were selected using a two-step feature selection approach, so as to build a linear discriminant analysis (LDA) classifier. The final features subset included one third of the total amount of extracted autonomic markers, mostly acquired during incremental exercise and active recovery, thus evidencing the relevance of these test segments in BS patients classification. The derived predictive model showed an improved performance with respect to previous works in the field (AUC = 0.92 ± 0.01; Se = 0.91 ± 0.06; Sp = 0.90 ± 0.05). Therefore, based on these findings, some of the analyzed HRV markers and the proposed model could be useful for risk stratification in Brugada syndrome.

[1]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[2]  Ornella Rimoldi,et al.  Abnormal Myocardial Presynaptic Norepinephrine Recycling in Patients With Brugada Syndrome , 2004, Circulation.

[3]  P. Coumel,et al.  Decreased nocturnal standard deviation of averaged NN intervals. An independent marker to identify patients at risk in the Brugada Syndrome. , 2003, European heart journal.

[4]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[5]  Ulrich Gergs,et al.  Autonomic Dysfunction in Patients with Brugada Syndrome: Further Biochemical Evidence of Altered Signaling Pathways , 2011, Pacing and clinical electrophysiology : PACE.

[6]  G. Billman The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance , 2013, Front. Physio..

[7]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[8]  G. Moody,et al.  Clinical Validation of the ECG-Derived Respiration (EDR) Technique , 2008 .

[9]  M Borggrefe,et al.  Long-Term Prognosis of Patients Diagnosed With Brugada Syndrome: Results From the FINGER Brugada Syndrome Registry , 2010, Circulation.

[10]  Pablo Laguna,et al.  A robust method for ECG-based estimation of the respiratory frequency during stress testing , 2006, IEEE Transactions on Biomedical Engineering.

[11]  P. Thompson,et al.  Brugada Syndrome, Exercise, and Exercise Testing , 2015, Clinical cardiology.

[12]  L. Fauchier,et al.  Abnormal Nocturnal Heart Rate Variability and QT Dynamics in Patients with Brugada Syndrome , 2007, Pacing and clinical electrophysiology : PACE.

[13]  Wataru Shimizu,et al.  Augmented ST-segment elevation during recovery from exercise predicts cardiac events in patients with Brugada syndrome. , 2010, Journal of the American College of Cardiology.

[14]  W. Haskell,et al.  Autonomic contribution to heart rate recovery from exercise in humans. , 1982, Journal of applied physiology: respiratory, environmental and exercise physiology.

[15]  P. Mabo,et al.  Heart rate complexity analysis in Brugada syndrome during physical stress testing , 2017, Physiological measurement.

[16]  Muthiah Subramanian,et al.  The Utility of Exercise Testing in Risk Stratification of Asymptomatic Patients With Type 1 Brugada Pattern , 2017, Journal of cardiovascular electrophysiology.

[17]  J. Brugada,et al.  Right bundle-branch block and ST-segment elevation in leads V1 through V3: a marker for sudden death in patients without demonstrable structural heart disease. , 1998, Circulation.

[18]  Mireia Calvo,et al.  Ensemble classifier based on linear discriminant analysis for distinguishing Brugada syndrome patients according to symptomatology , 2016, 2016 Computing in Cardiology Conference (CinC).

[19]  Guy Carrault,et al.  Improving ECG Beats Delineation With an Evolutionary Optimization Process , 2010, IEEE Transactions on Biomedical Engineering.

[20]  Georgios Theodorakis,et al.  Disorders of the Autonomic Nervous System in Patients With Brugada Syndrome: A Pilot Study , 2010, Journal of cardiovascular electrophysiology.

[21]  M. Hori,et al.  Vagally mediated heart rate recovery after exercise is accelerated in athletes but blunted in patients with chronic heart failure. , 1994, Journal of the American College of Cardiology.

[22]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[23]  L. Sornmo,et al.  Analysis of Heart Rate Variability Using Time-Varying Frequency Bands Based on Respiratory Frequency , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Akinori Awazu,et al.  Deterioration of the circadian variation of heart rate variability in Brugada syndrome may contribute to the pathogenesis of ventricular fibrillation. , 2014, Journal of cardiology.

[25]  K. Nademanee,et al.  Heart rate variability in patients with Brugada syndrome in Thailand. , 2003, European heart journal.

[26]  Alfredo I. Hernández,et al.  Heart rate variability and repolarization characteristics in symptomatic and asymptomatic Brugada syndrome , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[27]  Jere H. Mitchell,et al.  Neural circulatory control during exercise: early insights , 2013, Experimental physiology.

[28]  Akinori Awazu,et al.  Risk stratification of ventricular fibrillation in Brugada syndrome using noninvasive scoring methods. , 2016, Heart rhythm.

[29]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[30]  P M Okin,et al.  Impaired heart rate response to graded exercise. Prognostic implications of chronotropic incompetence in the Framingham Heart Study. , 1996, Circulation.

[31]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[32]  Glen M. Davis,et al.  Cardiac Autonomic Responses during Exercise and Post-exercise Recovery Using Heart Rate Variability and Systolic Time Intervals—A Review , 2017, Front. Physiol..

[33]  K Shimomura,et al.  The circadian pattern of the development of ventricular fibrillation in patients with Brugada syndrome. , 1999, European heart journal.

[34]  Wataru Shimizu,et al.  HRS/EHRA/APHRS Expert Consensus Statement on the Diagnosis and Management of Patients with Inherited Primary Arrhythmia Syndromes , 2013 .

[35]  Joseph S Alpert,et al.  ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1997 Exercise Testing Guidelines). , 2002, Journal of the American College of Cardiology.

[36]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[37]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[38]  Antonio H. Costa,et al.  Design of time-frequency representations using a multiform, tiltable exponential kernel , 1995, IEEE Trans. Signal Process..

[39]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[40]  Wataru Shimizu,et al.  Brugada syndrome: report of the second consensus conference. , 2005, Heart rhythm.

[41]  WilhelmHaverkamp,et al.  Cardiac Autonomic Dysfunction in Brugada Syndrome , 2002 .

[42]  Kiyoshi Nakazawa,et al.  Autonomic imbalance as a property of symptomatic Brugada syndrome. , 2003, Circulation journal : official journal of the Japanese Circulation Society.

[43]  J. Naughton,et al.  Physical activity and the prevention of coronary heart disease. , 1972, Preventive medicine.

[44]  J. Brugada,et al.  Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome. A multicenter report. , 1992, Journal of the American College of Cardiology.

[45]  J. Ruijter,et al.  Exercise-Induced ECG Changes in Brugada Syndrome , 2009, Circulation. Arrhythmia and electrophysiology.

[46]  J. Brugada,et al.  Right Bundle-Branch Block and ST-Segment Elevation in Leads V 1 Through V 3 A Marker for Sudden Death in Patients Without Demonstrable Structural Heart Disease , 1998 .

[47]  Andrea Mazzanti,et al.  2015 ESC Guidelines for the Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death. , 2016, Revista espanola de cardiologia.

[48]  A. Aslani,et al.  Significance of cardiac autonomic neuropathy in risk stratification of Brugada syndrome. , 2008, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[49]  Eduardo Gil,et al.  Dynamic assessment of spontaneous baroreflex sensitivity by means of time-frequency analysis using either RR or pulse interval variability , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[50]  G. Breithardt,et al.  Cardiac Autonomic Dysfunction in Brugada Syndrome , 2002, Circulation.