Wavelet analysis of heart rate variability: Impact of wavelet selection

A pilot study to assess the origin of the spectral power increases of heart rate variability associated with transient changes in the R-R interval

Spectral analysis of heart rate variability (HRV) is used to assess cardiovascular autonomic function. In the power density spectrum calculated from a time series of the R-R interval (RRI), three main components are distinguished: very-low-frequency (VLF; 0.003-0.04 Hz), low-frequency (LF; 0.04-0.15 Hz), and high-frequency (HF; 0.15-0.4 Hz) components. However, the physiological correlates of these frequency components have yet to be determined. In this study, we conducted spectral analysis of data segments of various lengths (5, 30, 100, and 200 s) of the RRI time series during active standing. Because of the trade-off relationship between time and frequency resolution, the analysis of the RRI data segment shorter than 30 s was needed to identify the temporal relationships between individual transient increases in RRI and the resulting spectral power changes. In contrast, the segment of 200 s was needed to properly evaluate the magnitude of the increase in the VLF power. The results showed that a transient increase in the RRI was tightly associated with simultaneous increases in the powers of the VLF, LF, and HF components. We further found that the simultaneous power increases in these three components were caused by the arterial baroreceptor reflex responding to rapid blood pressure rise.

BaroWavelet: An R-based tool for dynamic baroreflex evaluation through wavelet analysis techniques

BACKGROUND AND OBJECTIVE

Baroreflex sensitivity constitutes an indicator of the function of the baroreceptor control mechanism of blood pressure levels. It can be computed after estimating heart rate and blood pressure variability. We propose a novel tool for the evaluation of baroreflex sensitivity using wavelet analysis methods. This tool, known as BaroWavelet, incorporates an algorithm proposal based on the analysis methodology of the RHRV software package, as well as other conventional techniques. Our objectives are to develop and evaluate the tool, by testing its ability to detect changes in baroreflex sensitivity in humans.

METHODS

The code for this tool was designed in the R programming environment and was organized into two analysis routines and a graphical interface. Simulated recordings of blood pressure and inter-beat intervals were employed for an initial evaluation of the tool in a controlled environment. Finally, similar recordings obtained during supine and orthostatic postural evaluations, from patients that belonged to the open-access EUROBAVAR data set, were analyzed.

RESULTS

BaroWavelet identified the scripted changes of the baroreflex sensitivity in the simulated data. The algorithm proposal was also able to better retain additional information regarding the dynamics of the baroreflex. In the EUROBAVAR subjects, baroreflex sensitivity components were significantly smaller during orthostatism when compared with the supine position.

CONCLUSIONS

BaroWavelet managed to characterize baroreflex dynamics from the recordings, which were consistent with the findings reported in the literature. This demonstrates its effectiveness to perform these analyses. We suggest that this tool may be of use in research and for the evaluation of baroreflex sensitivity with clinical and therapeutic purposes. The new tool is available at the official GitHub repository of the Autonomic Nervous System Unit of the University of Málaga (https://github.com/CIMES-USNA-UMA/BaroWavelet).

CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions

CardioRVAR is a new R package designed for the complete evaluation of closed-loop cardiovascular interactions and baroreflex sensitivity estimated from continuous non-invasive heart rate and blood pressure recordings. In this work, we highlight the importance of this software tool in the context of human cardiovascular and autonomic neurophysiology. A summary of the main algorithms that CardioRVAR uses are reviewed, and the workflow of this package is also discussed. We present the results obtained from this tool after its application in three clinical settings. These results support the potential clinical and scientific applications of this tool. The open-source tool can be downloaded from a public GitHub repository, as well as its specific Shiny application, CardioRVARapp. The open-source nature of the tool may benefit the future continuation of this work.

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.

Heart rate variability is not suitable as surrogate marker for pain intensity in patients with chronic pain

ABSTRACT

The search towards more objective outcome measurements and consequently surrogate markers for pain started decades ago; however, no generally accepted biomarker for pain has qualified yet. The goal is to explore the value of heart rate variability (HRV) as surrogate marker for pain intensity chronic pain setting. Pain intensity scores and HRV were collected in 366 patients with chronic pain, through a cross-sectional multicenter study. Pain intensity was measured with both the Visual Analogue Scale and Numeric Rating Scale, while 16 statistical HRV parameters were derived. Canonical correlation analysis was performed to evaluate the correlation between the dependent pain variables and the HRV parameters. Surrogacy was determined for each HRV parameter with point estimates between 0 and 1 whereby values close to 1 indicate a strong association between the surrogate and the true endpoint at the patient level. Weak correlations were revealed between HRV parameters and pain intensity scores. The highest surrogacy point estimate was found for mean heart rate as marker for average pain intensity on the Numeric Rating Scale with point estimates of 0.0961 (95% CI from 0.0384 to 0.1537) and 0.0209 (95% CI from 0 to 0.05) for patients without medication use, and medication use respectively. This study indicated that HRV parameters as separate entities are no suitable surrogacy candidates for pain intensity, in a population of chronic pain patients. Further potential surrogate candidates and clinical robust true endpoints should be explored, in order to find a surrogate measure for the highly individual pain experience.

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

Research on a physical activity tracking system based upon three-axis accelerometer for patients with leg ulcers

Venous leg ulcerations are a common problem, with high prevalence in the middle-aged and elderly population, and more attention on research of their physical activities has been paid, as they have great effects on the blood circulation of the lower limb. With enough, appropriate training, the chronic venous ulcerations in the lower limb can be avoided and alleviated, and venous hypertension can be reduced effectively. The study deals with a physical activity tracking system for the patients based on a three-axis accelerometer. The system uses a three-axis accelerometer, a microcontroller, and a wireless Bluetooth module to form a data acquisition platform to acquire accelerations of the lower limb movement, and sends it to a smart mobile phone via the wireless Bluetooth module. The system takes advantages of the smart mobile phone to guide the chronic venous leg ulcers to do prescribed rehabilitation exercises for the lower limb muscles, perform acceleration data preprocessing, wavelet transform and reconstruction, denoising and feature extraction, obtain the results of the rehabilitation exercises, and then give reasonable evaluation and judgment. It is helpful to treat underlying venous reflux, create such an environment that allows skin to grow across an ulcer, and accelerate ulcer healing process consequently.