Decoupling the influence of systemic variables in the peripheral and cerebral haemodynamics during ECMO procedure by means of oblique and orthogonal subspace projections

Increased Respiratory Modulation of Blood Pressure in Hypertensive Patients

Objective

Although the important role of respiratory modulation of the cardiovascular system in the development of hypertension has been demonstrated in animal studies, little research has assessed this modulation in essential hypertensive patients. We aimed to explore whether respiratory-related variations in cardiovascular variables are changed in hypertensive patients and their potential relationships with the respiratory pattern.

Methods

Respiration, ECG, and beat-to-beat blood pressure (BP) were simultaneously measured in 46 participants (24 hypertensive patients and 22 normotensive participants) during rest and a mental arithmetic task (MAT). Respiratory-triggered averaging and orthogonal subspace projection methods were used to assess the respiratory modulations of BP and heart rate (HR). Respiratory parameters including inspiratory time, expiratory time, respiratory rate and their variabilities were also characterized.

Results

The inspiratory time, expiratory time, respiratory rate and their variabilities were not different between hypertensive and normotensives. Additionally, the modulation of HR by respiration was also similar between the two groups. Hypertensive patients exhibited an amplified respiratory modulation of systolic BP (SBP), as assessed from the amplitude of respiratory-related changes and the percentage of the power of respiratory-related variation, and also reflected from the temporal pattern of respiratory modulation of SBP. The exaggerated respiratory-related variation of SBP in hypertensive patients accounted for ≈23% of the total power of SBP, producing an absolute change of ≈4.5 mmHg in SBP. MAT was characterized by decreased inspiratory time and increased variabilities of expiratory time and respiratory rate with no changes in the amplitude of respiratory modulations.

Conclusion

Hypertensive patients had excessive respiratory modulation of SBP, despite having similar respiratory pattern with normotensives. These findings highlight the importance of respiratory influence in BP variation and suggest that respiratory modulation of SBP may have prognostic information for cardiovascular events in hypertensive patients.

A New Framework for the Assessment of Cerebral Hemodynamics Regulation in Neonates Using NIRS

We present a new framework for the assessment of cerebral hemodynamics regulation (CHR) in neonates using near-infrared spectroscopy (NIRS). In premature infants, NIRS measurements have been used as surrogate variables for cerebral blood flow (CBF) in the assessment of cerebral autoregulation (CA). However, NIRS measurements only reflect changes in CBF under constant changes in arterial oxygen saturation (SaO2). This condition is unlikely to be met at the bedside in the NICU. Additionally, CA is just one of the different highly coupled mechanisms that regulate brain hemodynamics. Traditional methods for the assessment of CA do not take into account the multivariate nature of CHR, producing inconclusive results. In this study we propose a newly developed multivariate methodology for the assessment of CHR. This method is able to effectively decouple the influences of SaO2 from the NIRS measurements, and at the same time, produces scores indicating the strength of the coupling between the systemic variables and NIRS recordings. We explore the use of this method, and its derived scores, for the monitoring of CHR using data from premature infants who developed a grade III-IV intra-ventricular hemorrhage during the first 3 days of life.

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%).

A Review of Wavelet Transform Time-Frequency Methods for NIRS-Based Analysis of Cerebral Autoregulation

Near-infrared spectroscopy (NIRS) has been proposed as a suitable technique for the analysis of cerebral autoregulation as it provides a simpler acquisition methodology and more artifact-free signal. A number of sophisticated wavelet transform methods have recently emerged to quantify the cerebral autoregulation mechanism using NIRS and blood pressure signals. These provide an enhanced partitioning of signal information via the time-frequency plane, which facilitates improved extraction of the components of interest. This area is reviewed, and enhancements to this form of analysis are suggested.

A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG

GOAL

This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG.

METHODS

It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses the principal components of the QRS complexes, and it describes changes in their morphology caused by an increased sympathetic activity during apnea. The second novel feature extracts the information shared between respiration and heart rate using orthogonal subspace projections. Respiratory information is derived from the ECG by means of three state-of-the-art algorithms, which are implemented and compared here. All features are used as input to a least-squares support vector machines classifier, using an RBF kernel. In total, 80 ECG recordings were included in the study.

RESULTS

Accuracies of about 85% are achieved on a minute-by-minute basis, for two independent datasets including both hypopneas and apneas together. Separation between apnea and normal recordings is achieved with 100% accuracy. In addition to apnea classification, the proposed methodology determines the contamination level of each ECG minute.

CONCLUSION

The performances achieved are comparable with those reported in the literature for fully automated algorithms.

SIGNIFICANCE

These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.

Separation of respiratory influences from the tachogram: a methodological evaluation

The variability of the heart rate (HRV) is widely studied as it contains information about the activity of the autonomic nervous system (ANS). However, HRV is influenced by breathing, independently of ANS activity. It is therefore important to include respiratory information in HRV analyses in order to correctly interpret the results. In this paper, we propose to record respiratory activity and use this information to separate the tachogram in two components: one which is related to breathing and one which contains all heart rate variations that are unrelated to respiration. Several algorithms to achieve this have been suggested in the literature, but no comparison between the methods has been performed yet. In this paper, we conduct two studies to evaluate the methods' performances to accurately decompose the tachogram in two components and to assess the robustness of the algorithms. The results show that orthogonal subspace projection and an ARMAX model yield the best performances over the two comparison studies. In addition, a real-life example of stress classification is presented to demonstrate that this approach to separate respiratory information in HRV studies can reveal changes in the heart rate variations that are otherwise masked by differing respiratory patterns.

Preprocessing by means of subspace projections for continuous Cerebral Autoregulation assessment using NIRS

Cerebral Autoregulation (CA) refers to the capability of the brain to maintain a more or less stable cerebral blood flow (CBF), despite the changes in blood perfusion. Monitoring this mechanism is of vital importance, especially in neonates, in order to prevent damage due to ischemia or hemorrhage. In clinical practice near-infrared spectroscopy (NIRS) measurements are used as a surrogate measurement for CBF. However, NIRS signals are highly dependent on the variations in arterial oxygen saturation (SaO2). Therefore, only segments with relatively constant SaO2 are used for CA assessment; which limits the possibilities of the use of NIRS for online monitoring. In this paper we propose the use of subspace projections to subtract the influence of SaO2 from NIRS measurements. Since this approach will be used in an online monitoring system, this preprocessing is carried out in a window-by-window framework. However, the use of subspace projections in consecutive segments produces discontinuities; we propose a methodology to reduce these effects. Obtained results indicate that the proposed method reduces the effect of discontinuities between consecutive segments. In addition, this methodology is able to subtract the influence of SaO2 from NIRS measurements. This approach facilitates the introduction of NIRS for online CA assessment.