Analysis of physiological time series using wavelet transforms

Sloping land use affects the complexity of soil moisture and temperature changes in the loess hilly region of China

Various land use types have been implemented by the government in the loess hilly region of China to facilitate sustainable land use. Understanding the variability in soil moisture and temperature under various sloping land use types can aid the ecological restoration and sustainable utilization of sloping land resources. The objective of this study was to use approximate entropy (ApEn) to reveal the variations in soil moisture and temperature under different land use types, because ApEn only requires a short data series to obtain robust estimates, with a strong anti-interference ability. An experiment was conducted with four typical land use scenarios (i.e., soybean sloping field, maize terraced field, jujube orchard, and grassland) over two consecutive plant growing seasons (2014 and 2015), and the time series of soil moisture and temperature within different soil depth layers of each land use type were measured in both seasons. The results showed that the changing amplitude, degree of variation, and active layer of soil moisture in the 0–160 cm soil depth layer, as well as the changing amplitude and degree of variation of soil temperature in the 0–100 cm soil layer increased in the jujube orchard over the two growing seasons. The changing amplitude, degree of variation, and active layer of soil moisture all decreased in the maize terraced field, as did the changing amplitude and degree of variation of soil temperature. The ApEn of the soil moisture series was the lowest in the 0–160 cm soil layer in the maize terraced field, and the ApEn of the soil temperature series was the highest in the 0–100 cm layer in the jujube orchard in the two growing seasons. Finally, the jujube orchard soil moisture and temperature change process were more variable, whereas the changes in the maize terraced field were more stable, with a stable soil moisture and temperature. This work highlights the usefulness of ApEn for revealing soil moisture and temperature changes and to guide the management and development of sloping fields.

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.

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.

Heart rate variability: are you using it properly? Standardisation checklist of procedures

BACKGROUND

Heart rate variability is used as an assessment method for cardiac autonomic modulation. Since the Task Force's publication on heart rate variability in 1996, the European Heart Rhythm Association Position Paper in 2015 and a recent publication in 2017, attention has been paid to recommendations on using heart rate variability analysis methods, as well as their applications in different physiological conditions and clinical studies. This analysis has proved to be useful as a complementary tool for clinical evaluation and to assess the effect of non-pharmacological therapeutic interventions, such as physical exercise programmes, on cardiac autonomic modulation.

OBJECTIVE

The aim of this article is to make recommendations and to develop a checklist of normalisation procedures regarding the use of heart rate variability data collection and analysis methodology, focusing on the cardiology area and cardiac rehabilitation.

METHODS

Based on previous heart rate variability publications, this paper provides a description of the most common shortcomings of using the analysis methods and considers recommendations and suggestions on how to minimise these occurrences by using a specific checklist.

CONCLUSIONS

This article includes recommendations and a checklist regarding the use of heart rate variability collection and analysis methods. This work could help improve reporting on clinical evaluation and therapeutic intervention results and consequently, disseminate heart rate variability knowledge.

Phenomenology of neurophysiologic changes during surgical treatment of carotid stenosis using signal analysis

Objective

To describe the changes in the shape and topology of the somatosensory evoked potential (SSEP) during carotid endarterectomy, with particular reference to the time of clamping.

Methods

Routine intraoperative monitoring was performed on 30 patients undergoing carotid endarterectomy (15) or undergoing stenting (15) using median nerve SSEPs. Post-operatively the first and second derivatives of the potential were examined. Separate analysis of the SSEP using wavelets was also performed.

Results

In no instances did changes in the SSEP reach clinical significance. The first derivative showed significant changes that were temporally related to the clamp period. After clamping the 'velocity' was higher than baseline. There were changes in the wavelets related to the clamp period with more marked spectral edges at the conclusion of the procedure than baseline. In all instances the patient had a good clinical outcome.

Conclusions

Wavelet and derivative analysis of evoked potentials show changes that are not apparent with measures of amplitude and latency. The clinical relevance of these changes remains uncertain and await larger studies.

Significance

Increased velocity and spectral edges may be markers of increased cerebral blood flow, at least in the setting of pre-existing carotid stenosis.

Analysis of neural dynamics in mild cognitive impairment and Alzheimer's disease using wavelet turbulence

OBJECTIVE

Current diagnostic guidelines encourage further research for the development of novel Alzheimer's disease (AD) biomarkers, especially in its prodromal form (i.e. mild cognitive impairment, MCI). Magnetoencephalography (MEG) can provide essential information about AD brain dynamics; however, only a few studies have addressed the characterization of MEG in incipient AD.

APPROACH

We analyzed MEG rhythms from 36 AD patients, 18 MCI subjects and 27 controls, introducing a new wavelet-based parameter to quantify their dynamical properties: the wavelet turbulence.

MAIN RESULTS

Our results suggest that AD progression elicits statistically significant regional-dependent patterns of abnormalities in the neural activity (p < 0.05), including a progressive loss of irregularity, variability, symmetry and Gaussianity. Furthermore, the highest accuracies to discriminate AD and MCI subjects from controls were 79.4% and 68.9%, whereas, in the three-class setting, the accuracy reached 67.9%.

SIGNIFICANCE

Our findings provide an original description of several dynamical properties of neural activity in early AD and offer preliminary evidence that the proposed methodology is a promising tool for assessing brain changes at different stages of dementia.

QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study

BACKGROUND

Wider QRS and left bundle branch block morphology are related to response to cardiac resynchronization therapy (CRT). A novel time-frequency analysis of the QRS complex may provide additional information in predicting response to CRT.

METHODS

Signal-averaged electrocardiograms were prospectively recorded, before CRT, in orthogonal leads and QRS decomposition in three frequency bands was performed using the Morlet wavelet transformation.

RESULTS

Thirty eight patients (age 65±10years, 31 males) were studied. CRT responders (n=28) had wider baseline QRS compared to non-responders and lower QRS energies in all frequency bands. The combination of QRS duration and mean energy in the high frequency band had the best predicting ability (AUC 0.833, 95%CI 0.705-0.962, p=0.002) followed by the maximum energy in the high frequency band (AUC 0.811, 95%CI 0.663-0.960, p=0.004).

CONCLUSIONS

Wavelet transformation of the QRS complex is useful in predicting response to CRT.

Feature selection using a genetic algorithm in a motor imagery-based Brain Computer Interface

This study performed an analysis of several feature extraction methods and a genetic algorithm applied to a motor imagery-based Brain Computer Interface (BCI) system. Several features can be extracted from EEG signals to be used for classification in BCIs. However, it is necessary to select a small group of relevant features because the use of irrelevant features deteriorates the performance of the classifier. This study proposes a genetic algorithm (GA) as feature selection method. It was applied to the dataset IIb of the BCI Competition IV achieving a kappa coefficient of 0.613. The use of a GA improves the classification results using extracted features separately (kappa coefficient of 0.336) and the winner competition results (kappa coefficient of 0.600). These preliminary results demonstrated that the proposed methodology could be useful to control motor imagery-based BCI applications.

Combining predictive capabilities of transcranial doppler with electrocardiogram to predict hemorrhagic shock

Hemorrhagic shock (HS) potentially impacts the chance of survival in most traumatic injuries. Thus, it is highly desirable to maximize the survival rate in cases of blood loss by predicting the occurrence of hemorrhagic shock with biomedical signals. Since analyzing one physiological signal may not enough to accurately predict blood loss severity, two types of physiological signals - Electrocardiography (ECG) and Transcranial Doppler (TCD) - are used to discover the degree of severity. In this study, these degrees are classified as mild, moderate and severe, and also severe and non-severe. The data for this study were generated using the human simulated model of hemorrhage, which is called lower body negative pressure (LBNP). The analysis is done by applying discrete wavelet transformation (DWT). The wavelet-based features are defined using the detail and approximate coefficients and machine learning algorithms are used for classification. The objective of this study is to evaluate the improvement when analyzing ECG and TCD physiological signals together to classify the severity of blood loss. The results of this study show a prediction accuracy of 85.9% achieved by support vector machine in identifying severe/non-severe states.

Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states

Backpropagation artificial neural network (ANN) has been designed to classify sleep-wake stages. Four hours continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) from conscious subjects were digitally recorded and stored in computer. EOG and EMG signals were used for manual identification of sleep states before training and testing of ANN. The percentages power of the 2 s epochs of the digitized EEG signals from each of three sleep-wake patterns, sleep spindles (SS), rapid eye movement (REM) sleep and awake (AWA) sates, were calculated and analyzed to select the manually confirmed sleep-wake states for each epoch. Further, second order Daubechies mother wavelet has been used to get the wavelet coefficients for the selected EEG epochs. The wavelet coefficients for the EEG epochs (64 data) were selected as inputs for the training the network and to classify SS, REM sleep and AWA stages. The ANN architecture used (64-14-3) in present study shows overall very good agreement with manual sleep stage scoring with an average of 95.35% for all the 1,140 samples tested from SS, REM and AWA stages. This architecture of ANN was also found effectively differentiating the EEG power spectra from different sleep-wake states (96.84% in SS, 93.68% in REM sleep, 95.52% in AWA state). The high performance observed with the system based on wavelet coefficients along with the ANN, highlights the need of this computational tool into the field of sleep research.

Study of the EEG changes during the combined ingestion of alcohol and H1-antihistamines by using the wavelet transform

H(1)-antihistamines affect the central nervous system (CNS) and, therefore, electroencephalographic (EEG) changes should be expected to occur. The principal aim of this work was to assess the effects on the EEG when hydroxyzine 10 mg (HY) and cetirizine 25 mg (CE) were administered with and without alcohol 0.8 g/kg (AL). Thirty-three healthy young subjects participated in two placebo-controlled trials. In the first one, 15 subjects received placebo (PL), HY and CE. In the second trial, 18 volunteers took PL, AL, and AL in combination with HY and CE. CNS effects of the different treatment conditions were evaluated at baseline, as well as at +4 h and +1 h post-medication for each study, respectively. EEG recordings from electrodes O1 and O2 were analyzed using the wavelet transform. Then, several entropies were calculated from wavelet decomposition to detect changes in the pattern of regularity of the signals. The obtained results suggest that the concomitant ingestion of AL with HY reduces the changes in the irregularity of the EEG, opposite to the behavior observed for CE. Hence, wavelet entropies could be useful descriptors of the EEG alterations induced by several drugs in a different way that the conventional Fourier-based methods.

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

The two principal challenges of neonatal physiologic monitoring device are: (1) the development of computational strategies that consider the rudimentary forms of neonatal sleep state especially for preterm infants and (2) any physiologic monitoring device for clinical applications in a neonatal intensive care setting must be small, portable, and user-friendly. Our multicenter neonatal sleep consortium has acquired more than 1,100 multihour EEG-sleep recordings on over 370 neonates, ranging from 24 to 44 weeks gestation. Each recording was visually-scored for state, arousals, movements, and rapid eye movements, which were used as templates when applying spectral analyses. The authors have defined a brain dysmaturity index to represent functional brain reorganization as the prenate matures to a full-term age; delayed or accelerated physiologic behaviors have been described for the preterm cohort when compared to the full-term group at the same postmenstrual age. Seven EEG-sleep measures comprise this index: quiet sleep percentage, sleep cycle length, rapid eye movements, arousals, spectral beta EEG energies, spectral EEG correlations, and a spectral measure of respiratory regularity. Linear and nonlinear computational algorithms are being developed to automate the computation of the dysmaturity index and to identify new feature types that correlate with dysmaturity. Automated neonatal sleep monitoring system can potentially improve neonatal neurointensive care by facilitating analyses of pervasive neonatal brain disorders expressed primarily as altered sleep state organization, and help predict altered developmental trajectories of children at higher risk for neurologic sequelae.

Wavelet packet transform for R-R interval variability

INTRODUCTION

Wavelet transform is used for time-frequency analysis. Recently, discrete wavelet transform (DWT) has been used to analyze R-R interval or heart rate variability. However, we hypothesized that wavelet packet transform (WPT) is a better way to analyze such variability. In the present study, we compared resolution of frequency band and amplitude, which are used for analysis of the variability, with DWT and WPT, followed by Hilbert transform.

METHODS

A chirp signal which covers all frequency bands used for R-R interval variability was employed as a simulated signal. Levels 1-6 of DWT and level 3 of WPT were used for signal analysis. Amplitudes of the gained signal were evaluated with Hilbert transform. Differences in error of the gained amplitude from expected amplitude between CWT and DWT for low-frequency (LF) and high-frequency (HF) components were compared. To evaluate time-dependent changes in R-R interval variability, head-up tilt (HUT) was employed as an orthostatic challenge.

RESULTS

Errors for both HF and LF, derived from the simulated signal with WPT, were significantly smaller than those of DWT. With HUT, time dependent changes in LF, HF, and LF/HF were observed.

DISCUSSION

Although DWT is a valuable method for time-frequency analysis, WPT is a more appropriate method to utilize wavelet transform due to the equivalent resolution of the gained frequency band. WPT for time-frequency analysis improves analysis of time-dependent changes in R-R interval variability.

Variability analyses suggest that supraspino-spinal interactions provide dynamic stability in motor control

Effects of supraspino-spinal feedforward-feedback (FF-FB) interactions on variability in locomotor rhythm and coordination were examined in in vitro brain-spinal cord lamprey preparations. Spinal locomotor networks were activated by applying 0.2 mM N-methyl-DL-aspartate (NMA) to three spinal pools: gill, rostral and caudal. Bathing the brain with zero Ca(2+) saline altered supraspinal-spinal drive and FF-FB interaction while spino-supraspinal feedback was changed by applying NMA to the caudal pool only. Wavelet analyses indicated a non-uniform energy distribution in ventral root (VR) activity that shifted between frequency bands on FF-FB interruption. Wavelet analysis was used to extract 300-s long epochs of low frequency burst rhythm. These were analyzed using a sliding-window time-varying covariance method. From the autocovariance in each window, the cycle period and height of the first side lobe peak were determined. Rostral VR variability (determined from standard deviation and coefficient of variation of all cycle periods and the mean peak height) was significantly higher than caudal VR variability. FF-FB interruption significantly decreased the rostral VR cycle period and variability but the rostro-caudal gradient remained. The intersegmental delay was also affected. The caudal VR rhythm with NMA in the caudal pool only was slower but more variable than with NMA over the entire cord. These results indicate that the locomotor rhythm in the presence of supraspino-spinal interactions is slower but has a higher variability. The higher variability may reflect a dynamic stability of the system. Additionally, differences in local neural organization likely contribute to rostro-caudal differences in variability of the motor output.

Wavelet time entropy, T wave morphology and myocardial ischemia

Using wavelets, we computed the entropy of the signal at various frequency levels (wavelet time entropy) and, thus, find an optimal measure to differentiate normal states from ischemic ones. This new indicator is independent from the ST segment and yet provide a conclusive detection of the ischemic states.

Wavelet-based noise removal for biomechanical signals: a comparative study

The purpose of this paper is to present wavelet-based noise removal (WBNR) techniques to remove noise from biomechanical acceleration signals obtained from numerical differentiation of displacement data. Manual and semiautomatic methods were used to determine thresholds for both orthogonal and biorthogonal filters. This study also compares the performance of WBNR approaches with four automatic conventional noise removal techniques used in biomechanics. The conclusion of this work is that WBNR techniques are very effective in removing noise from differentiated signals with sharp transients while leaving these transients intact. For biomechanical signals with certain characteristics, WBNR techniques perform better than conventional methods, as indicated by quantitative merit measures.

Predicting coronary disease risk based on short-term RR interval measurements: a neural network approach

Coronary heart disease is a multifactorial disease and it remains the most common cause of death in many countries. Heart rate variability has been used for non-invasive measurement of parasympathetic activity and prediction of cardiac death. Patterns of heart rate variability associated with respiratory sinus arrhythmia have recently been considered as possible indicators of coronary heart disease risk in asymptomatic subjects. The aim of this work is to detect individuals at varying risk of coronary heart disease based on short-term heart rate variability measurements under controlled respiration. Artificial neural networks are used to recognise Poincaré-plot-encoded heart rate variability patterns related to coronary heart disease risk. The results indicate a relatively coarse binary representation of Poincaré plots could be superior to an analogue encoding which, in principle, carries more information.