Smoothed pseudo Wigner-Ville distribution as an alternative to Fourier transform in rats

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition

Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes automatic extraction and classification of features through the use of different convolutional neural networks (CNNs). At first, the proposed method converts the filtered EEG signals into an image using a time-frequency representation. Smoothed pseudo-Wigner-Ville distribution is used to transform time-domain EEG signals into images. These images are fed to pretrained AlexNet, ResNet50, and VGG16 along with configurable CNN. The performance of four CNNs is evaluated by measuring the accuracy, precision, Mathew's correlation coefficient, F1-score, and false-positive rate. The results obtained by evaluating four CNNs show that configurable CNN requires very less learning parameters with better accuracy. Accuracy scores of 90.98%, 91.91%, 92.71%, and 93.01% obtained by AlexNet, ResNet50, VGG16, and configurable CNN show that the proposed method is best among other existing methods.

Classification of Post-Anterior Cruciate Ligament Reconstruction Running Dynamics using Non-Traditional Features

Despite extensive rehabilitation, nearly half of all post-anterior cruciate ligament reconstruction (ACLR) individuals are unable to perform dynamic tasks at the level they did prior to their injury. This inability can be attributed to unresolved neuromuscular deficits that manifest as altered limb dynamics. While traditional discrete metrics; such as peak vertical ground reaction force (vGRF) and peak knee flexion angle, have been used to successfully differentiate between healthy and pathological running dynamics, recent studies have shown that non-traditional metrics derived from autoregressive (AR) modeling and Smoothed Pseudo Wigner-Ville (SPWV) analysis techniques can also successfully delineate between healthy and pathological populations and could potentially possess greater sensitivity than the traditional metrics. Thus, the objective of this study was to compare the performance of classification models generated from traditional and nontraditional metrics collected from healthy controls and post-ACLR individuals during a running protocol. We hypothesized that the non-traditional metric-based classification model would outperform the traditional metric based model. Thirty-one controls and 31 post-ACLR individuals performed a running protocol from which the traditional metrics - peak vGRF, linear vGRF loading rate and peak knee flexion angle - and nontraditional metrics - dynamic vGRF ratio, AR model coefficients, and a SPWV derived low frequency-high frequency ratio - were extracted from vGRF and knee flexion running waveforms. The results indicated that a fine Gaussian SVM classification model derived from the non-traditional metrics had an accuracy of 87%, specificity of 83% and sensitivity of 61% and it outperformed the classification model derived from traditional metrics. These findings indicate that additional, valuable information can be ascertained from non-traditional metrics that evaluate waveform dynamics. Additionally, it suggests that this or similar models can be used to track the restoration of healthy running dynamics in post-ACLR individuals during rehabilitation.

Using Time-Frequency Analysis to Characterize Altered Knee Dynamics in Post ACL Reconstruction Individuals

More than 250,000 individuals suffer an anterior cruciate ligament (ACL) injury in the United States each year requiring surgery and rehabilitation. However, despite exhaustive rehabilitation individuals are often plagued by neuromuscular deficits that lead to detrimental knee loading and knee osteoarthritis. Traditionally, time domain-based metrics like peak sagittal plane knee angle are used to quantify differences in knee mechanics; however, additional information can potentially be elucidated from time-frequency analyses. Here Smoothed Pseudo Wigner-Ville (SPWV), a time-frequency analysis technique, was used to investigate differences in knee loading dynamics between healthy controls and post ACL reconstruction individuals during running. The results indicated that post ACL reconstruction individuals adopt significantly different loading strategies in their injured limb than their non-injured limb. Individuals adopt a stiffer, more restrictive movement strategy delineated by a stronger low frequency to high frequency (LF/HF) ratio while the non-injured limb exhibit a more oscillatory motion (p<; 0.001). The time domain metrics were unable to identify differences between the ACL injured and non-injured limbs. The ability of SPWV to provide both quantitative and visual means to detect these differences supports its use as a clinical tool to track and monitor joint health.

Esmolol pretreatment attenuates heart rate increase and parasympathetic inhibition during rapid increases in desflurane concentration: A preliminary randomized study

BACKGROUND

Rapid increases in desflurane concentration can transiently increase the heart rate (HR). Esmolol possesses a high β1-adrenoceptor selectivity and a short duration of action. This preliminary study aimed at investigating the effects of esmolol on the HR and autonomic modulation during a desflurane-induced HR increase.

METHODS

American Society of Anesthesiologists physical status I female subjects, aged 20 to 50 years, who were undergoing minor breast surgery were randomly assigned to 2 groups. Rapid increases in desflurane concentration were commenced after induction of anesthesia. Each subject received either i.v. saline (control group) or esmolol 0.5 mg/kg (esmolol group) before desflurane inhalation. Using time-frequency spectral analysis of HR variability, the HR indices were studied at baseline, postinduction, posttreatment, as well as at minimal alveolar concentrations of desflurane reaching 1.0, 1.3, and 1.5. The low frequency (LF) power is influenced by both the sympathetic and parasympathetic activity, whereas the high frequency (HF) power reflects the parasympathetic activity. The LF/HF ratio is thought to reflect either sympathovagal balance or sympathetic modulation.

RESULTS

Electrocardiograms for data analysis were obtained from 8 subjects in each group. Rapid increases in desflurane concentration after induction caused a HR increase. Both the corresponding LF and HF powers were low and the LF/HF ratio remained unchanged. This indicates that the desflurane-induced HR increase may be attributed to parasympathetic inhibition and may be independent of sympathetic activation. Esmolol pretreatment effectively attenuated desflurane-induced HR increase. Moreover, subjects receiving esmolol pretreatment had increased LF and HF powers, but did not have changes in their LF/HF ratios, as compared to those without esmolol.

CONCLUSION

Esmolol pretreatment attenuates HR increase and parasympathetic inhibition during rapid increases in desflurane concentration.

Time-frequency distribution properties of event-related potentials in mental fatigue induced by visual memory tasks

Prolonged periods of demanding cognitive tasks lead to an exhausted feeling known as mental fatigue. The neural underpinnings of mental fatigue are still under exploration. In the present study, we aimed to identify neurophysiological indicators of mental fatigue by studying the time-frequency distribution of the event-related potentials (ERPs) measured in N=26 adults in nonfatigued versus fatigued states. We were interested in the frontal theta and occipital alpha variations, which have shown consistent relationships with mental fatigue in previous studies. Furthermore, we expected differential changes in left and right electrodes, in line with previously detected lateralization effects in cognitive tasks. Mental fatigue was induced by a sustained two-back verbal visual memory task for 125 min and assessed using the Chalder Fatigue Scale. We applied a high-resolution time-frequency analysis method called smoothed pseudo Wigner Ville distribution and used regional integrals as indicators for changing trends of signal energy. Results showed an increase in ERP frontal theta energy (P=0.03) and a decrease in occipital alpha energy (P=0.028) when participants became mentally fatigued. The change in frontal theta was more pronounced in left electrode sites (P=0.032), hinting toward a differential fatigue effect in the two hemispheres. The results were discussed on the basis of previous lateralization studies with memory tasks and interpreted as an indicator of a causal relationship between the sustained task execution and the physiological changes. Our findings also suggest that the ERP signal energy variations in frontal theta and occipital alpha might be used as neural biomarkers to assess mental fatigue.

The integration of the internal and external milieu in the insula during dynamic emotional experiences

Whilst external events trigger emotional responses, interoception (the perception of internal physiological states) is fundamental to core emotional experience. By combining high resolution functional neuroimaging with concurrent physiological recordings, we investigated the neural mechanisms of interoceptive integration during free listening to an emotionally salient audio film. We found that cardiac activity, a key interoceptive signal, was robustly synchronised across participants and centrally represented in the posterior insula. Effective connectivity analysis revealed that the anterior insula, specifically tuned to the emotionally salient moments of the audio stream, serves as an integration hub of interoceptive processing: interoceptive states represented in the posterior insula are integrated with exteroceptive representations by the anterior insula to highlight these emotionally salient moments. Our study for the first time demonstrates the insular hierarchy for interoceptive processing during natural emotional experience. These findings provide an ecologically-valid framework for elucidating the neural underpinnings of emotional deficits in neuropsychiatric disorders.

Time-frequency methods for coherent spectroscopy

Time-frequency decomposition techniques, borrowed from the signal-processing field, have been adapted and applied to the analysis of 2D oscillating signals. While the Fourier-analysis techniques available so far are able to interpret the information content of the oscillating signal only in terms of its frequency components, the time-frequency transforms (TFT) proposed in this work can instead provide simultaneously frequency and time resolution, unveiling the dynamics of the relevant beating components, and supplying a valuable help in their interpretation. In order to fully exploit the potentiality of this method, several TFTs have been tested in the analysis of sample 2D data. Possible artifacts and sources of misinterpretation have been identified and discussed.

Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks

The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time-frequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time-frequency domain for determination of fatigue. Time-frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.

Heart rate variability in rodents: uses and caveats in toxicological studies

Heart rate variability (HRV) is a measure of cardiac pacing dynamics that has recently garnered a great deal of interest in environmental health studies. While the use of these measures has become popular, much uncertainty remains in the interpretation of results, both in terms of human and animal research. In humans, HRV endpoints, specifically chronic alterations in baseline HRV patterns, have been reasonably well characterized as prognostic indicators of adverse outcomes for a variety of diseases. However, such information is lacking for reversible HRV changes that may be induced by short-term exposures to environmental toxicants. Furthermore, there are minimal substantive data, either acute or chronic, regarding the pathological interpretation or prognostic value of toxicant-induced changes in HRV in rodents. The present report summarizes the physiological and clinical aspects of HRV, the methodological processes for obtaining these endpoints, and previous human and animal studies in the field of environmental health. Furthermore, we include a discussion of important caveats and recommendations for the interpretation of HRV data in animal research.

Empirical mode decomposition to assess cardiovascular autonomic control in rats

Heart beat rate and blood pressure, together with baroreflex sensitivity, have become important tools in assessing cardiac autonomic system control and in studying sympathovagal balance. These analyses are usually performed thanks to spectral indices computed from standard spectral analysis techniques. However, standard spectral analysis and its corresponding rigid band-pass filter formulation suffer from two major drawbacks. It can be significantly distorted by non-stationarity issues and it proves unable to adjust to natural intra- and inter-individual variability. Empirical mode decomposition (EMD), a tool recently introduced in the literature, provides us with a signal-adaptive decomposition that proves useful for the analysis of non-stationary data and shows a strong capability to precisely adjust to the spectral content of the analyzed data. It is based on the concept that any complicated set of data can be decomposed into a finite number of components, called intrinsic mode functions, associated with different spectral contributions. The aims of this study were twofold. First, we studied the changes in the sympathovagal balance induced by various pharmacological blockades (phentolamine, atropine and atenolol) of the autonomic nervous system in normotensive rats. Secondly, we assessed the use of EMD for the analysis of the cardiac sympathovagal balance after pharmacological injections. For this, we developed a new (EMD-based) low frequency vs. high frequency spectral decomposition of heart beat variability and systolic blood pressure, we define the corresponding EMD spectral indices and study their relevance to detect and analyze changes accurately in the sympathovagal balance without having recourse to any a priori fixed high-pass/low-pass filters.

Impairment of Instantaneous Autonomic Regulation Relates to Blood Pressure Fall Immediately after Standing in the Elderly and Hypertensives

The relation between changes in blood pressure and changes in autonomic activity over a very short period of time has not been reported thus far. To examine this relation, we here introduced a new method of power spectrum analysis with wavelet transformation, which has very fine time resolution and is able to assess changes in autonomic activity quantitatively even during movement. Our subjects were 15 hypertensive and 17 normotensive subjects. A head-up tilt test was performed in all subjects, and during the test, electrocardiogram and blood pressure were recorded continuously. The power spectrums for both parameters were calculated simultaneously every 5 s using wavelet transformation. The high frequency of the RR interval of the electrocardiogram (RR-HF) and low frequency of systolic blood pressure (SBP-LF) were defined and calculated as markers of parasympathetic and alpha-1 receptor blocker, bunazosin-sensitive sympathetic activity, respectively. Focusing on the changes for 2 min immediately after head-up tilting, it was found that the changes in SBP-LF and RR-HF were significantly delayed, by at least 40 s, in hypertensives compared with normotensives and also in elderly compared with non-elderly subjects. Multiple regression analysis demonstrated that the instantaneous change in RR-HF was the most important confounding factor for a fall in blood pressure immediately after head-up tilting. In conclusion, real-time changes in autonomic activity calculated by wavelet transformation may provide sensitive and useful information about acute changes in cardiovascular regulation, such as delayed reaction of the autonomic regulation after head-up tilting, that may be major causes of the blood pressure fall in hypertensive and elderly subjects.