The correlation of vectorcardiographic changes to blood lactate concentration during an exercise test

Abstract In this study, the correlations between blood lactate concentration (BLC), different vector electrocardiogram (VECG) parameters, ventilatory parameters and heart rate during exercise and recovery periods were investigated. The aim was to clarify the relationships between VECG parameters and different exercise intensity markers. Six (25–37 years old) nonathlete, healthy, male participants took part in the study. All participants performed two different bicycle ergospirometric protocols (P1 and P2) in order to attain different lactate levels with different heart rate profiles. A principal component regression (PCR) approach is introduced for preprocessing the VECG components. PCR was compared to Sawitzcy Golay and wavelet filtering methods using simulated data. The performance of the PCR approach was clearly better in low signal-to-noise ratio (SNR) situations, and thus, it enables reliable VECG estimates even during intensive exercise. As a result, strong positive mean individual correlations between BLC and T-wave kurtosis (P1: r  = 0.86 and P2: r  = 0.8, p r  = −0.7, P2: r  = −0.62, p

[1]  Carlo Castagna,et al.  Heart rate and blood lactate correlates of perceived exertion during small-sided soccer games. , 2009, Journal of science and medicine in sport.

[2]  L. Codecá,et al.  Determination of the anaerobic threshold by a noninvasive field test in runners. , 1982, Journal of applied physiology: respiratory, environmental and exercise physiology.

[3]  Liviu Goras,et al.  An ECG Signals Compression Method and Its Validation Using NNs , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Mika P. Tarvainen,et al.  A Principal Component Regression Approach for Estimation of Ventricular Repolarization Characteristics , 2010, IEEE Transactions on Biomedical Engineering.

[5]  L. Lehtola,et al.  Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG , 2008, Journal of medical engineering & technology.

[6]  Philip Constantinou,et al.  Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals , 2011, IEEE Transactions on Information Technology in Biomedicine.

[7]  P A Karjalainen,et al.  Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects. , 2011, Physiological measurement.

[8]  Z.A. Jaffery,et al.  Performance Comparision of Wavelet Threshold Estimators for ECG Signal Denoising , 2010, 2010 International Conference on Advances in Recent Technologies in Communication and Computing.

[9]  I. Jolliffe Principal Component Analysis , 2002 .

[10]  H. Blackburn,et al.  Waveform Patterns in Frank‐Lead Rest and Exercise Electrocardiograms of Healthy Elderly Men , 1973, Circulation.

[11]  Mika P. Tarvainen,et al.  An advanced detrending method with application to HRV analysis , 2002, IEEE Transactions on Biomedical Engineering.

[12]  T. Seppänen,et al.  Dynamics and Rate‐Dependence of the Spatial Angle between Ventricular Depolarization and Repolarization Wave Fronts during Exercise ECG , 2010, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[13]  J. Doust,et al.  The Conconi test in not valid for estimation of the lactate turnpoint in runners. , 1997, Journal of sports sciences.

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

[15]  O Meste,et al.  Assessment of ventilatory thresholds during graded and maximal exercise test using time varying analysis of respiratory sinus arrhythmia , 2005, British Journal of Sports Medicine.

[16]  H. P. Gildein,et al.  Anaerobic threshold and maximal steady-state blood lactate in prepubertal boys , 2004, European Journal of Applied Physiology and Occupational Physiology.

[17]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[18]  Richard B. Devereux,et al.  Principal Component Analysis of the T Wave and Prediction of Cardiovascular Mortality in American Indians: The Strong Heart Study , 2002, Circulation.

[19]  M. Malik,et al.  Spatial, temporal and wavefront direction characteristics of 12-lead T-wave morphology , 1999, Medical & Biological Engineering & Computing.

[20]  M. Simoons,et al.  Gradual Changes of ECG Waveform During and After Exercise in Normal Subjects , 1975, Circulation.

[21]  I. Johnstone,et al.  Ideal denoising in an orthonormal basis chosen from a library of bases , 1994 .

[22]  M. Ramasubba Reddy,et al.  A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's , 2000, IEEE Transactions on Biomedical Engineering.

[23]  D. Cambier,et al.  Validity of the Heart Rate Deflection Point As a Predictor of Lactate Threshold Concepts During Cycling , 2004, Journal of strength and conditioning research.

[24]  T. Seppänen,et al.  Effects of controlled hypoglycaemia on cardiac repolarisation in patients with type 1 diabetes , 2008, Diabetologia.

[25]  Ernestfrank An Accurate, Clinically Practical System For Spatial Vectorcardiography , 1956 .

[26]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[27]  Yoshiyuki Matsuura,et al.  Relationships of anaerobic threshold and onset of blood lactate accumulation with endurance performance , 2004, European Journal of Applied Physiology and Occupational Physiology.

[28]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[29]  Olaf Dössel,et al.  Investigation of parameters highlighting drug induced small changes of the T-wave's morphology for drug safety studies , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  F Cottin,et al.  Assessment of ventilatory thresholds from heart rate variability in well-trained subjects during cycling. , 2006, International journal of sports medicine.

[31]  T M McLellan,et al.  Ventilatory and Plasma Lactate Response with Different Exercise Protocols: A Comparison of Methods , 1985, International journal of sports medicine.

[32]  K. Preidler,et al.  Relationship between heart rate threshold, lactate turn point and myocardial function. , 1994, International journal of sports medicine.

[33]  M. Wilburne,et al.  The ischemic T loop during and following exercise--a vector-electrocardiographic (VECG) study. , 1968, Journal of electrocardiology.

[34]  G. Saha,et al.  Fetal ECG extraction from single-channel maternal ECG using singular value decomposition , 1997, IEEE Transactions on Biomedical Engineering.

[35]  Gunnar Borg,et al.  The increase of perceived exertion, aches and pain in the legs, heart rate and blood lactate during exercise on a bicycle ergometer , 2006, European Journal of Applied Physiology and Occupational Physiology.

[36]  Gavin Sandercock,et al.  Cardiac vagal activity following three intensities of exercise in humans , 2010, Clinical physiology and functional imaging.

[37]  F Cottin,et al.  Ventilatory thresholds assessment from heart rate variability during an incremental exhaustive running test. , 2007, International journal of sports medicine.

[38]  J. Doust,et al.  Lack of Reliability in Conconi's Heart Rate Deflection Point , 1995, International journal of sports medicine.

[39]  Katerina Hnatkova,et al.  Ventricular gradient and nondipolar repolarization components increase at higher heart rate. , 2004, American journal of physiology. Heart and circulatory physiology.