Stress classification by separation of respiratory modulations in heart rate variability using orthogonal subspace projection

The influence of respiration on the heart rate is a phenomenon known as respiratory sinus arrhythmia. However, effects of respiration are often ignored in studies of heart rate variability. In this paper, we take respiratory influences into account by separating the tachogram in two components, one related to respiration and one residual component, using orthogonal subspace projection. We demonstrate that it is important to remove respiratory influences during classification of rest and mental stress. Using merely the original tachogram, the classification accuracy is 57.13%, while the use of the residual tachogram results in an almost perfect classification (accuracy = 97.88%).

[1]  Bernhard Dahme,et al.  Implementation and Interpretation of Respiratory Sinus Arrhythmia Measures in Psychosomatic Medicine: Practice Against Better Evidence? , 2006, Psychosomatic medicine.

[2]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[3]  Sabine Van Huffel,et al.  Extraction of direct respiratory influences form the tachogram using multiscale principal component analysis , 2012 .

[4]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[5]  Gregory F. Lewis,et al.  Statistical strategies to quantify respiratory sinus arrhythmia: Are commonly used metrics equivalent? , 2012, Biological Psychology.

[6]  J. Hirsch,et al.  Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. , 1981, The American journal of physiology.

[7]  Sabine Van Huffel,et al.  Decoupling the influence of systemic variables in the peripheral and cerebral haemodynamics during ECMO procedure by means of oblique and orthogonal subspace projections , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[9]  S. Van Huffel,et al.  Multiscale principal component analysis to separate respiratory influences from the tachogram: Application to stress monitoring , 2012, 2012 Computing in Cardiology.

[10]  P. Grossman,et al.  Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions , 2007, Biological Psychology.

[11]  S. Huffel,et al.  Instantaneous changes in heart rate regulation due to mental load in simulated office work , 2011, European Journal of Applied Physiology.

[12]  Elke Vlemincx,et al.  Sigh rate and respiratory variability during mental load and sustained attention. , 2011, Psychophysiology.

[13]  R. Gutierrez-Osuna,et al.  Removal of Respiratory Influences From Heart Rate Variability in Stress Monitoring , 2011, IEEE Sensors Journal.

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

[15]  G. Baselli,et al.  Spectral decomposition in multichannel recordings based on multivariate parametric identification , 1997, IEEE Transactions on Biomedical Engineering.