Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude

Central sleep apnea: pathophysiologic classification

Central sleep apnea is not a single disorder; it can present as an isolated disorder or as a part of other clinical syndromes. In some conditions, such as heart failure, central apneic events are due to transient inhibition of ventilatory motor output during sleep, owing to the overlapping influences of sleep and hypocapnia. Specifically, the sleep state is associated with removal of wakefulness drive to breathe; thus, rendering ventilatory motor output dependent on the metabolic ventilatory control system, principally PaCO2. Accordingly, central apnea occurs when PaCO2 is reduced below the "apneic threshold". Our understanding of the pathophysiology of central sleep apnea has evolved appreciably over the past decade; accordingly, in disorders such as heart failure, central apnea is viewed as a form of breathing instability, manifesting as recurrent cycles of apnea/hypopnea, alternating with hyperpnea. In other words, ventilatory control operates as a negative-feedback closed-loop system to maintain homeostasis of blood gas tensions within a relatively narrow physiologic range, principally PaCO2. Therefore, many authors have adopted the engineering concept of "loop gain" (LG) as a measure of ventilatory instability and susceptibility to central apnea. Increased LG promotes breathing instabilities in a number of medical disorders. In some other conditions, such as with use of opioids, central apnea occurs due to inhibition of rhythm generation within the brainstem. This review will address the pathogenesis, pathophysiologic classification, and the multitude of clinical conditions that are associated with central apnea, and highlight areas of uncertainty.

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.

Relationship between cardiorespiratory phase coherence during hypoxia and genetic polymorphism in humans

KEY POINTS

High altitude-induced hypoxia in humans evokes a pattern of breathing known as periodic breathing (PB), in which the regular oscillations corresponding to rhythmic expiration and inspiration are modulated by slow periodic oscillations. The phase coherence between instantaneous heart rate and respiration is shown to increase significantly at the frequency of periodic breathing during acute and sustained normobaric and hypobaric hypoxia. It is also shown that polymorphism in specific genes, NOTCH4 and CAT, is significantly correlated with this coherence, and thus with the incidence of PB. Differences in phase shifts between blood flow signals and respiratory and PB oscillations clearly demonstrate contrasting origins of the mechanisms underlying normal respiration and PB. These novel findings provide a better understanding of both the genetic and the physiological mechanisms responsible for respiratory control during hypoxia at altitude, by linking genetic factors with cardiovascular dynamics, as evaluated by phase coherence.

ABSTRACT

Periodic breathing (PB) occurs in most humans at high altitudes and is characterised by low-frequency periodic alternation between hyperventilation and apnoea. In hypoxia-induced PB the dynamics and coherence between heart rate and respiration and their relationship to underlying genetic factors is still poorly understood. The aim of this study was to investigate, through novel usage of time-frequency analysis methods, the dynamics of hypoxia-induced PB in healthy individuals genotyped for a selection of antioxidative and neurodevelopmental genes. Breathing, ECG and microvascular blood flow were simultaneously monitored for 30 min in 22 healthy males. The same measurements were repeated under normoxic and hypoxic (normobaric (NH) and hypobaric (HH)) conditions, at real and simulated altitudes of up to 3800 m. Wavelet phase coherence and phase difference around the frequency of breathing (approximately 0.3 Hz) and around the frequency of PB (approximately 0.06 Hz) were evaluated. Subjects were genotyped for common functional polymorphisms in antioxidative and neurodevelopmental genes. During hypoxia, PB resulted in increased cardiorespiratory coherence at the PB frequency. This coherence was significantly higher in subjects with NOTCH4 polymorphism, and significantly lower in those with CAT polymorphism (HH only). Study of the phase shifts clearly indicates that the physiological mechanism of PB is different from that of the normal respiratory cycle. The results illustrate the power of time-evolving oscillatory analysis content in obtaining important insight into high altitude physiology. In particular, it provides further evidence for a genetic predisposition to PB and may partly explain the heterogeneity in the hypoxic response.

Respiratory Rate Derived from Pulse Photoplethysmographic Signal by Pulse Decomposition Analysis

A novel technique to derive respiratory rate from pulse photoplethysmographic (PPG) signals is presented. It exploits some morphological features of the PPG pulse that are known to be modulated by respiration: amplitude, slope transit time, and width of the main wave, and time to the first reflected wave. A pulse decomposition analysis technique is proposed to measure these features. This technique allows to decompose the PPG pulse into its main wave and its subsequent reflected waves, improving the robustness against noise and morphological changes that usually occur in long-term recordings. Proposed methods were evaluated with a data base containing PPG and plethysmography-based respiratory signals simultaneously recorded during a paced-breathing experiment. Results suggest that normal ranges of spontaneous respiratory rate (0.1-0.5 Hz) can be accurately estimated (median and interquartile range of relative error less than 5%) from PPG signals by using the studied features.

Detection of exercise periodic breathing using thermal flowmeter in patients with heart failure

Exercise periodic breathing (EPB) is associated with exercise intolerance and poor prognosis in patients with heart failure (HF). However, EPB detection during cardiopulmonary exercise test (CPET) is difficult. The present study investigated the use of a wireless monitoring device to record the EPB during CPET and proposed quantization parameter estimates for the EPB. A total of 445 patients with HF were enrolled and underwent exercise tests. The ventilation data from the wearable device were compared with the data obtained during the CPET and were analyzed based on professional opinion and on 2 automated programs (decision tree [DT] and oscillatory pattern methods). The measurement accuracy was greater with the DT method (89 %) than with the oscillatory pattern method (75 %). The cutoffs for EPB recognition using the DT method were (1) an intercept of the regression line passing through the minute ventilation rate vs. the time curve during the recovery phase ≥64.63, and (2) an oscillatory phase duration to total exercise time ratio ≥0.5828. The wearable device was suitable for the assessment of EPB in patients with HF, and our new automated analysis system using the DT method effectively identified the EPB pattern.