Point process time-frequency analysis of dynamic respiratory patterns during meditation practice.

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov-Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.

Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics.

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.

Orthogonal basis expansion based atrial activity reconstruction for atrial fibrillation electrocardiogram analysis.

We propose a novel algorithm for extracting atrial activity from single lead electrocardiogram (ECG) signal sustained with atrial fibrillation (AF), based on a short-time expansion of an orthogonal basis function set. The method preserves the time variation of spectral content of the underlying AF signal, thus time-frequency analysis of the AF signal can be successfully performed. The new method is compared to the standard average beat subtraction (ABS) method using synthetic AF sustained ECG data. The orthogonal basis expansion method has a higher correlation with the original AF signal compared to the ABS method for a range of signal to noise ratio (SNR) levels, and correlation is improved by 16% at an SNR of 0dB. Time-frequency analysis of the reconstructed AF signal based on Bessel distribution also shows the superiority of the orthogonal basis expansion method over ABS.