Heart rate variability characterization using a time-frequency based instantaneous frequency estimation technique

In this paper, a new method for characterizing the newborn heart rate variability (HRV) is proposed. The central of the method is the newly proposed technique for instantaneous frequency (IF) estimation specifically designed for nonstationary multicomponen signals such as HRV. The new method attempts to characterize the newborn HRV using features extracted from the time–frequency (TF) domain of the signal. These features comprise the IF, the instantaneous bandwidth (IB) and instantaneous energy (IE) of the different TF components of the HRV. Applied to the HRV of both normal and seizure suffering newborns, this method clearly reveals the locations of the spectral peaks and their time-varying nature. The total energy of HRV components, ET and ratio of energy concentrated in the low-frequency (LF) to that in high frequency (HF) components have been shown to be significant features in identifying the HRV of newborn with seizures.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

Role of peripheral afference during acquisition of a complex coordination task

It has long been supposed that the interference observed in certain patterns of coordination is mediated, at least in part, by peripheral afference from the moving limbs. We manipulated the level of afferent input, arising from movement of the opposite limb, during the acquisition of a complex coordination task. Participants learned to generate flexion and extension movements of the right wrist, of 75degrees amplitude, that were a quarter cycle out of phase with a 1-Hz sinusoidal visual reference signal. On separate trials, the left wrist either was at rest, or was moved passively by a torque motor through 50degrees, 75degrees or 100degrees, in synchrony with the reference signal. Five acquisition sessions were conducted on successive days. A retention session was conducted I week later. Performance was initially superior when the opposite limb was moved passively than when it was static. The amplitude and frequency of active movement were lower in the static condition than in the driven conditions and the variation in the relative phase relation across trials was greater than in the driven conditions. In addition, the variability of amplitude, frequency and the relative phase relation during each trial was greater when the opposite limb was static than when driven. Similar effects were expressed in electromyograms. The most marked and consistent differences in the accuracy and consistency of performance (defined in terms of relative phase) were between the static condition and the condition in which the left wrist was moved through 50degrees. These outcomes were exhibited most prominently during initial exposure to the task. Increases in task performance during the acquisition period, as assessed by a number of kinematic variables, were generally well described by power functions. In addition, the recruitment of extensor carpi radialis (ECR), and the degree of co-contraction of flexor carpi radialis and ECR, decreased during acquisition. Our results indicate that, in an appropriate task context, afferent feedback from the opposite limb, even when out of phase with the focal movement, may have a positive influence upon the stability of coordination.

Neural compensation for compliant loads during rhythmic movement

An experiment was performed to characterise the movement kinematics and the electromyogram (EMG) during rhythmic voluntary flexion and extension of the wrist against different compliant (elastic-viscous-inertial) loads. Three levels of each type of load, and an unloaded condition, were employed. The movements were paced at a frequency of I Hz by an auditory metronome, and visual feedback of wrist displacement in relation to a target amplitude of 100degrees was provided. Electro-myographic recordings were obtained from flexor carpi radialis (FCR) and extensor carpi radialis brevis (ECR). The movement profiles generated in the ten experimental conditions were indistinguishable, indicating that the CNS was able to compensate completely for the imposed changes in the task dynamics. When the level of viscous load was elevated, this compensation took the form of an increase in the rate of initial rise of the flexor and the extensor EMG burst. In response to increases in inertial load, the flexor and extensor EMG bursts commenced and terminated earlier in the movement cycle, and tended to be of greater duration. When the movements were performed in opposition to an elastic load, both the onset and offset of EMG activity occurred later than in the unloaded condition. There was also a net reduction in extensor burst duration with increases in elastic load, and an increase in the rate of initial rise of the extensor burst. Less pronounced alterations in the rate of initial rise of the flexor EMG burst were also observed. In all instances, increases in the magnitude of the external load led to elevations in the overall level of muscle activation. These data reveal that the elements of the central command that are modified in response to the imposition of a compliant load are contingent, not only upon the magnitude, but also upon the character of the load.

Potential dementia biomarkers based on the time-varying microstructure of sleep EEG spindles

Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007

Electro-oscillographic correlation between dorsal raphe nucleus, neocortex and hippocampus during wakefulness before and after serotoninergic inactivation

Theta rhythm in many brain structures characterizes wakefulness and desynchronized sleep in most subprimate mammalian brains. In close relation to behaviors, theta frequency and voltage undergo a fine modulation which may involve mobilization of dorsal raphe nucleus efferent pathways. In the present study we analyzed frequency modulation (through instantaneous frequency variation) of theta waves occurring in three cortical areas, in hippocampal CA1 and in the dorsal raphe nucleus of Wistar rats during normal wakefulness and after injection of the 5-HT1a receptor agonist 8-OH-DPAT into the dorsal raphe. We demonstrated that in attentive states the variation of theta frequency among the above structures is highly congruent, whereas after 8-OH-DPAT injection, although regular signals are present, the variation is much more complex and shows no relation to behaviors. Such functional uncoupling after blockade demonstrates the influence of dorsal raphe nucleus efferent serotoninergic fibers on the organization of alertness, as evaluated by electro-oscillographic analysis.

Increased coherence among striatal regions in the theta range during attentive wakefulness

The striatum, the largest component of the basal ganglia, is usually subdivided into associative, motor and limbic components. However, the electrophysiological interactions between these three subsystems during behavior remain largely unknown. We hypothesized that the striatum might be particularly active during exploratory behavior, which is presumably associated with increased attention. We investigated the modulation of local field potentials (LFPs) in the striatum during attentive wakefulness in freely moving rats. To this end, we implanted microelectrodes into different parts of the striatum of Wistar rats, as well as into the motor, associative and limbic cortices. We then used electromyograms to identify motor activity and analyzed the instantaneous frequency, power spectra and partial directed coherence during exploratory behavior. We observed fine modulation in the theta frequency range of striatal LFPs in 92.5 ± 2.5% of all epochs of exploratory behavior. Concomitantly, the theta power spectrum increased in all striatal channels (P < 0.001), and coherence analysis revealed strong connectivity (coefficients >0.7) between the primary motor cortex and the rostral part of the caudatoputamen nucleus, as well as among all striatal channels (P < 0.001). Conclusively, we observed a pattern of strong theta band activation in the entire striatum during attentive wakefulness, as well as a strong coherence between the motor cortex and the entire striatum. We suggest that this activation reflects the integration of motor, cognitive and limbic systems during attentive wakefulness.

Encodage des forces tactiles dans le cortex somatosensoriel primaire

Les deux fonctions principales de la main sont la manipulation d’objet et l’exploration tactile. La détection du glissement, rapportée par les mécanorécepteurs de la peau glabre, est essentielle pour l’exécution de ces deux fonctions. Durant la manipulation d’objet, la détection rapide du micro-glissement (incipient slip) amène la main à augmenter la force de pince pour éviter que l’objet ne tombe. À l’opposé, le glissement est un aspect essentiel à l’exploration tactile puisqu’il favorise une plus grande acuité tactile. Pour ces deux actions, les forces normale et tangentielle exercées sur la peau permettent de décrire le glissement mais également ce qui arrive juste avant qu’il y ait glissement. Toutefois, on ignore comment ces forces contrôlées par le sujet pourraient être encodées au niveau cortical. C’est pourquoi nous avons enregistré l’activité unitaire des neurones du cortex somatosensoriel primaire (S1) durant l’exécution de deux tâches haptiques chez les primates. Dans la première tâche, deux singes devaient saisir une pastille de métal fixe et y exercer des forces de cisaillement sans glissement dans une de quatre directions orthogonales. Des 144 neurones enregistrés, 111 (77%) étaient modulés à la direction de la force de cisaillement. L’ensemble de ces vecteurs préférés s’étendait dans toutes les directions avec un arc variant de 50° à 170°. Plus de 21 de ces neurones (19%) étaient également modulés à l’intensité de la force de cisaillement. Bien que 66 neurones (59%) montraient clairement une réponse à adaptation lente et 45 autres (41%) une réponse à adaptation rapide, cette classification ne semblait pas expliquer la modulation à l’intensité et à la direction de la force de cisaillement. Ces résultats montrent que les neurones de S1 encodent simultanément la direction et l’intensité des forces même en l’absence de glissement. Dans la seconde tâche, deux singes ont parcouru différentes surfaces avec le bout des doigts à la recherche d’une cible tactile, sans feedback visuel. Durant l’exploration, les singes, comme les humains, contrôlaient les forces et la vitesse de leurs doigts dans une plage de valeurs réduite. Les surfaces à haut coefficient de friction offraient une plus grande résistance tangentielle à la peau et amenaient les singes à alléger la force de contact, normale à la peau. Par conséquent, la somme scalaire des composantes normale et tangentielle demeurait constante entre les surfaces. Ces observations démontrent que les singes contrôlent les forces normale et tangentielle qu’ils appliquent durant l’exploration tactile. Celles-ci sont également ajustées selon les propriétés de surfaces telles que la texture et la friction. Des 230 neurones enregistrés durant la tâche d’exploration tactile, 96 (42%) ont montré une fréquence de décharge instantanée reliée aux forces exercées par les doigts sur la surface. De ces neurones, 52 (54%) étaient modulés avec la force normale ou la force tangentielle bien que l’autre composante orthogonale avait peu ou pas d’influence sur la fréquence de décharge. Une autre sous-population de 44 (46%) neurones répondait au ratio entre la force normale et la force tangentielle indépendamment de l’intensité. Plus précisément, 29 (30%) neurones augmentaient et 15 (16%) autres diminuaient leur fréquence de décharge en relation avec ce ratio. Par ailleurs, environ la moitié de tous les neurones (112) étaient significativement modulés à la direction de la force tangentielle. De ces neurones, 59 (53%) répondaient à la fois à la direction et à l’intensité des forces. L’exploration de trois ou quatre différentes surfaces a permis d’évaluer l’impact du coefficient de friction sur la modulation de 102 neurones de S1. En fait, 17 (17%) neurones ont montré une augmentation de leur fréquence de décharge avec l’augmentation du coefficient de friction alors que 8 (8%) autres ont montré le comportement inverse. Par contre, 37 (36%) neurones présentaient une décharge maximale sur une surface en particulier, sans relation linéaire avec le coefficient de friction des surfaces. La classification d’adaptation rapide ou lente des neurones de S1 n’a pu être mise en relation avec la modulation aux forces et à la friction. Ces résultats montrent que la fréquence de décharge des neurones de S1 encode l’intensité des forces normale et tangentielle, le ratio entre les deux composantes et la direction du mouvement. Ces résultats montrent que le comportement d’une importante sous-population des neurones de S1 est déterminé par les forces normale et tangentielle sur la peau. La modulation aux forces présentée ici fait le pont entre les travaux évaluant les propriétés de surfaces telles que la rugosité et les études touchant à la manipulation d’objets. Ce système de référence s’applique en présence ou en absence de glissement entre la peau et la surface. Nos résultats quant à la modulation des neurones à adaptation rapide ou lente nous amènent à suggérer que cette classification découle de la manière que la peau est stimulée. Nous discuterons aussi de la possibilité que l’activité des neurones de S1 puisse inclure une composante motrice durant ces tâches sensorimotrices. Finalement, un nouveau cadre de référence tridimensionnel sera proposé pour décrire et rassembler, dans un même continuum, les différentes modulations aux forces normale et tangentielle observées dans S1 durant l’exploration tactile.

Design and Development of re configurable compact cross patch antenna for switchable polarization

Antennas are necessary and vital components of communication and radar systems, but sometimes their inability to adjust to new operating scenarios can limit system performance. Reconfigurable antennas can adjust with changing system requirements or environmental conditions and provide additional levels of functionality that may result in wider instantaneous frequency bandwidths, more extensive scan volumes, and radiation patterns with more desirable side lobe distributions. Their agility and diversity created new horizons for different types of applications especially in cognitive radio, Multiple Input Multiple Output Systems, satellites and many other applications. Reconfigurable antennas satisfy the requirements for increased functionality, such as direction finding, beam steering, radar, control and command, within a confined volume. The intelligence associated with the reconfigurable antennas revolved around switching mechanisms utilized. In the present work, we have investigated frequency reconfigurable polarization diversity antennas using two methods: 1. By using low-loss, high-isolation switches such as PIN diode, the antenna can be structurally reconfigured to maintain the elements near their resonant dimensions for different frequency bands and/or polarization. 2. Secondly, the incorporation of variable capacitors or varactors, to overcome many problems faced in using switches and their biasing. The performances of these designs have been studied using standard simulation tools used in industry/academia and they have been experimentally verified. Antenna design guidelines are also deduced by accounting the resonances. One of the major contributions of the thesis lies in the analysis of the designed antennas using FDTD based numerical computation to validate their performance.

EMD: A novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency-energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Application of EMD as a novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Increased coherence among striatal regions in the theta range during attentive wakefulness

The striatum, the largest component of the basal ganglia, is usually subdivided into associative, motor and limbic components. However, the electrophysiological interactions between these three subsystems during behavior remain largely unknown. We hypothesized that the striatum might be particularly active during exploratory behavior, which is presumably associated with increased attention. We investigated the modulation of local field potentials (LFPs) in the striatum during attentive wakefulness in freely moving rats. To this end, we implanted microelectrodes into different parts of the striatum of Wistar rats, as well as into the motor, associative and limbic cortices. We then used electromyograms to identify motor activity and analyzed the instantaneous frequency, power spectra and partial directed coherence during exploratory behavior. We observed fine modulation in the theta frequency range of striatal LFPs in 92.5 ± 2.5% of all epochs of exploratory behavior. Concomitantly, the theta power spectrum increased in all striatal channels (P < 0.001), and coherence analysis revealed strong connectivity (coefficients >0.7) between the primary motor cortex and the rostral part of the caudatoputamen nucleus, as well as among all striatal channels (P < 0.001). Conclusively, we observed a pattern of strong theta band activation in the entire striatum during attentive wakefulness, as well as a strong coherence between the motor cortex and the entire striatum. We suggest that this activation reflects the integration of motor, cognitive and limbic systems during attentive wakefulness.

Self-validating technique for the measurement of the linewidth enhancement factor in semiconductor lasers

A new method for measuring the linewidth enhancement factor (α-parameter) of semiconductor lasers is proposed and discussed. The method itself provides an estimation of the measurement error, thus self-validating the entire procedure. The α-parameter is obtained from the temporal profile and the instantaneous frequency (chirp) of the pulses generated by gain switching. The time resolved chirp is measured with a polarization based optical differentiator. The accuracy of the obtained values of the α-parameter is estimated from the comparison between the directly measured pulse spectrum and the spectrum reconstructed from the chirp and the temporal profile of the pulse. The method is applied to a VCSEL and to a DFB laser emitting around 1550 nm at different temperatures, obtaining a measurement error lower than ± 8%.