Individual Time-Dependent Spectral Boundaries for Improved Accuracy in Time-Frequency Analysis of Heart Rate Variability

Heart rate variability spectrum characteristics in children with sleep apnea

BACKGROUND

Classic spectral analysis of heart rate variability (HRV) in pediatric sleep apnea-hypopnea syndrome (SAHS) traditionally evaluates the very low frequency (VLF: 0-0.04 Hz), low frequency (LF: 0.04-0.15 Hz), and high frequency (HF: 0.15-0.40 Hz) bands. However, specific SAHS-related frequency bands have not been explored.

METHODS

One thousand seven hundred and thirty-eight HRV overnight recordings from two pediatric databases (0-13 years) were evaluated. The first one (981 children) served as training set to define new HRV pediatric SAHS-related frequency bands. The associated relative power (RP) were computed in the test set, the Childhood Adenotonsillectomy Trial database (CHAT, 757 children). Their relationships with polysomnographic variables and diagnostic ability were assessed.

RESULTS

Two new specific spectral bands of pediatric SAHS within 0-0.15 Hz were related to duration of apneic events, number of awakenings, and wakefulness after sleep onset (WASO), while an adaptive individual-specific new band from HF was related to oxyhemoglobin desaturations, arousals, and WASO. Furthermore, these new spectral bands showed improved diagnostic ability than classic HRV.

CONCLUSIONS

Novel spectral bands provide improved characterization of pediatric SAHS. These findings may pioneer a better understanding of the effects of SAHS on cardiac function and potentially serve as detection biomarkers.

IMPACT

New specific heart rate variability (HRV) spectral bands are identified and characterized as potential biomarkers in pediatric sleep apnea. Spectral band BW1 (0.001-0.005 Hz) is related to macro sleep disruptions. Spectral band BW2 (0.028-0.074 Hz) is related to the duration of apneic events. An adaptive spectral band within the respiratory range, termed ABW3, is related to oxygen desaturations. The individual and collective diagnostic ability of these novel spectral bands outperforms classic HRV bands.

Noninvasive Cardiorespiratory Signals Analysis for Asthma Evolution Monitoring in Preschool Children

OBJECTIVE

Despite its increasing prevalence, diagnosis of asthma in children remains problematic due to their difficulties in producing repeatable spirometric maneuvers. Moreover, low adherence to inhaled corticosteroids (ICS) treatment could result in permanent airway remodeling. The growing interest in a noninvasive and objective way for monitoring asthma, together with the apparent role of autonomic nervous system (ANS) in its pathogenesis, have attracted interest towards heart rate variability (HRV) and cardiorespiratory coupling (CRC) analyses.

METHODS

HRV and CRC were analyzed in 68 children who were prescribed ICS treatment due to recurrent obstructive bronchitis. They underwent three different electrocardiogram and respiratory signals recordings, during and after treatment period. After treatment completion, they were followed up during 6 months and classified attending to their current asthma status.

RESULTS

Vagal activity, as measured from HRV, and CRC, were reduced after treatment in those children at lower risk of asthma, whereas it kept unchanged in those with a worse prognosis.

CONCLUSION

Results suggest that HRV analysis could be useful for the continuous monitoring of ANS anomalies present in asthma, thus contributing to evaluate the evolution of the disease, which is especially challenging in young children.

SIGNIFICANCE

Noninvasive ANS assessment using HRV analysis could be useful in the continuous monitoring of asthma in children.

Development of a "Thermal-Associated Pain Index" score using infrared-thermography for objective pain assessment

Without any doubt, research in biomedical engineering and anesthesiology achieved diverse ground-breaking successes for the sake of patient safety and for optimization of medical treatment in the last decades. Particularly anesthesia has become increasingly comfortable and safer due to new monitoring devices and further techniques. However, assessment of pain still relies on self-reporting of the patient using a Numeric Rating Scale ranging from 0 to 10. Obviously, this method suffers from severe restraints when unconscious, anesthetized or uncooperative subjects or children are involved as patients. Furthermore, no continuous monitoring is available so that features like alerting telemetry are lacking. Several scientific groups and companies searched intensively for procedures to measure pain objectively. Skin conductance, heart rate variability and peripheral perfusion, among others, were used to develop new algorithms and devices. Up to date, none of these devices succeeded to enter in clinical routine. In this project, we used infrared thermography (IRT) to analyze facial expressions and further thermal-associated phenomena that are visible in recorded IRT sequences such as lacrimation and perspiration. By means of clinical observations, a number of IRT features were predefined that were expected to correlate with pain. The combination of those features led to the so-called "Thermal-Associated Pain Intensity" (TAPI) after normalization and transformation. The TAPI correlates significantly with the NRS and achieves a sensitivity of above 0.75 to detect pain.

Nocturnal Heart Rate Variability Spectrum Characterization in Preschool Children With Asthmatic Symptoms

Asthma is a chronic lung disease that usually develops during childhood. Despite that symptoms can almost be controlled with medication, early diagnosis is desirable in order to reduce permanent airway obstruction risk. It has been suggested that abnormal parasympathetic nervous system (PSNS) activity might be closely related with the pathogenesis of asthma, and that this PSNS activity could be reflected in cardiac vagal control. In this work, an index to characterize the spectral distribution of the high frequency (HF) component of heart rate variability (HRV), named peakness ($\wp$), is proposed. Three different implementations of $\wp$, based on electrocardiogram (ECG) recordings, impedance pneumography (IP) recordings and a combination of both, were employed in the characterization of a group of preschool children classified attending to their risk of developing asthma. Peakier components were observed in the HF band of those children classified as high-risk ( $p < 0.005$), who also presented reduced sympathvoagal balance. Results suggest that high-risk of developing asthma might be related with a lack of adaptability of PSNS.

Electrocardiographic features for the measurement of drivers' mental workload

This study examines the effect of mental workload on the electrocardiogram (ECG) of participants driving the Lane Change Task (LCT). Different levels of mental workload were induced by a secondary task (n-back task) with three levels of difficulty. Subjective data showed a significant increase of the experienced workload over all three levels. An exploratory approach was chosen to extract a large number of rhythmical and morphological features from the ECG signal thereby identifying those which differentiated best between the levels of mental workload. No single rhythmical or morphological feature was able to differentiate between all three levels. A group of parameters were extracted which were at least able to discriminate between two levels. For future research, a combination of features is recommended to achieve best diagnosticity for different levels of mental workload.

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome

Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.

Considerations in the assessment of heart rate variability in biobehavioral research

Heart rate variability (HRV) refers to various methods of assessing the beat-to-beat variation in the heart over time, in order to draw inference on the outflow of the autonomic nervous system. Easy access to measuring HRV has led to a plethora of studies within emotion science and psychology assessing autonomic regulation, but significant caveats exist due to the complicated nature of HRV. Firstly, both breathing and blood pressure regulation have their own relationship to social, emotional, and cognitive experiments – if this is the case are we observing heart rate (HR) changes as a consequence of breathing changes? Secondly, experiments often have poor internal and external controls. In this review we highlight the interrelationships between HR and respiration, as well as presenting recommendations for researchers to use when collecting data for HRV assessment. Namely, we highlight the superior utility of within-subjects designs along with the importance of establishing an appropriate baseline and monitoring respiration.

Spectral Boundary Adaptation on Heart Rate Variability for Sleep and Wake Classification

A method of adapting the boundaries when extracting the spectral features from heart rate variability (HRV) for sleep and wake classification is described. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Conventionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity which in turn may limit their discriminative power, e.g. in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) vary over time. In order to minimize the impact of these variations, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments were conducted on a data set acquired from two groups with 15 healthy and 15 insomnia subjects each. Results show that adapting the HRV spectral features significantly increased their discriminative power when classifying sleep and wake. Additionally, this method also provided a significant improvement of the overall classification performance when used in combination with other HRV non-spectral features. Furthermore, compared with the use of actigraphy, the classification performed better when combining it with the HRV features.

Increasing sensitivity in the measurement of heart rate variability: the method of non-stationary RR time-frequency analysis

A novel method of the time-frequency analysis of non-stationary heart rate variability (HRV) is developed which introduces the fragmentary spectrum as a measure that brings together the frequency content, timing and duration of HRV segments. The fragmentary spectrum is calculated by the similar basis function algorithm. This numerical tool of the time to frequency and frequency to time Fourier transformations accepts both uniform and non-uniform sampling intervals, and is applicable to signal segments of arbitrary length. Once the fragmentary spectrum is calculated, the inverse transform recovers the original signal and reveals accuracy of spectral estimates. Numerical experiments show that discontinuities at the boundaries of the succession of inter-beat intervals can cause unacceptable distortions of the spectral estimates. We have developed a measure that we call the "RR deltagram" as a form of the HRV data that minimises spectral errors. The analysis of the experimental HRV data from real-life and controlled breathing conditions suggests transient oscillatory components as functionally meaningful elements of highly complex and irregular patterns of HRV.

Heart rate variability dynamics during low-dose propofol and dexmedetomidine anesthesia

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods

In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time-frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time-frequency method. This paper is an excursus through the most common time-frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking.

Spectral analysis of time series of events: effect of respiration on heart rate in neonates

Certain types of biomedical processes such as the heart rate generator can be considered as signals that are sampled by the occurring events, i.e. QRS complexes. This sampling property generates problems for the evaluation of spectral parameters of such signals. First, the irregular occurrence of heart beats creates an unevenly sampled data set which must either be pre-processed (e.g. by using trace binning or interpolation) prior to spectral analysis, or analyzed with specialized methods (e.g. Lomb's algorithm). Second, the average occurrence of events determines the Nyquist limit for the sampled time series. Here we evaluate different types of spectral analysis of recordings of neonatal heart rate. Coupling between respiration and heart rate and the detection of heart rate itself are emphasized. We examine both standard and data adaptive frequency bands of heart rate signals generated by models of coupled oscillators and recorded data sets from neonates. We find that an important spectral artifact occurs due to a mirror effect around the Nyquist limit of half the average heart rate. Further we conclude that the presence of respiratory coupling can only be detected under low noise conditions and if a data-adaptive respiratory band is used.

Analysis of heart rate variability using time-varying frequency bands based on respiratory frequency

In this paper a methodological approach for the analysis of nonstationary heart rate variability (HRV) signals using time-varying frequency bands based on respiratory frequency is presented. Spectral analysis of HRV is accomplished by means of the Smoothed Pseudo Wigner Ville distribution. Different approaches to the definition of the low frequency (LF) and high frequency (HF) bands are considered which involve respiratory information, derived either from a respiratory signal or from the ECG itself. Results are presented which derive from recordings acquired during stress testing and induced emotion experiments.

Evaluation of Autonomic Function Underlying Slow Postexercise Heart Rate Recovery

The reduction in heart rate (HR) during the first minute of recovery immediately after a graded maximal exercise stress test (GXT) has recently been found to be a powerful and independent predictor of cardiovascular and all-cause mortality. Reduced vagal activity has been postulated as the cause, but this has not been proven in a population with slow HR recovery (HRR).

PURPOSE

To investigate autonomic contributions to HRR using time-frequency analysis in a group of individuals demonstrating slow HRR.

METHODS

HRR was defined as the difference in HR between peak exercise and 1 min later; a value < or = 18 bpm was set as threshold and considered abnormal. A modified continuous wavelet transform (CWT) was used to perform time-dependent spectral analysis during the baseline steady state and the following non-steady-state conditions created by GXT. This method provides dynamic measures of low-frequency (LF) and high-frequency (HF) peaks associated with autonomic activity. Individuals (N = 20) with a previous slow HRR underwent a second GXT within 3 months after their initial test. An additional eight subjects whose first GXT disclosed normal HRR were taken as a control group.

RESULTS

Seven of 20 subjects demonstrated slow HRR (14 +/- 5 bpm) on the repeat test, and 13 subjects displayed normal HRR (29 +/- 5 bpm). Subjects with slow HRR in both GXT displayed significantly (P < 0.05) lower HF and LF fluctuations during recovery than those with normal HRR.

CONCLUSIONS

Attenuated HRR after GXT, assessed by CWT, is indeed associated with abnormal vagal reactivation and prolonged sympathetic stimulation after termination of maximal exercise.