The cerebral circulation of the newborn studied by electrical impedance plethysmography

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Instantaneous frequency based newborn EEG seizure characterisation

The electroencephalogram (EEG), used to noninvasively monitor brain activity, remains the most reliable tool in the diagnosis of neonatal seizures. Due to their nonstationary and multi-component nature, newborn EEG seizures are better represented in the joint time-frequency domain than in either the time domain or the frequency domain. Characterising newborn EEG seizure nonstationarities helps to better understand their time-varying nature and, therefore, allow developing efficient signal processing methods for both modelling and seizure detection and classification. In this article, we used the instantaneous frequency (IF) extracted from a time-frequency distribution to characterise newborn EEG seizures. We fitted four frequency modulated (FM) models to the extracted IFs, namely a linear FM, a piecewise-linear FM, a sinusoidal FM, and a hyperbolic FM. Using a database of 30-s EEG seizure epochs acquired from 35 newborns, we were able to show that, depending on EEG channel, the sinusoidal and piecewise-linear FM models best fitted 80-98% of seizure epochs. To further characterise the EEG seizures, we calculated the mean frequency and frequency span of the extracted IFs. We showed that in the majority of the cases (>95%), the mean frequency resides in the 0.6-3 Hz band with a frequency span of 0.2-1 Hz. In terms of the frequency of occurrence of the four seizure models, the statistical analysis showed that there is no significant difference(p = 0.332) between the two hemispheres. The results also indicate that there is no significant differences between the two hemispheres in terms of the mean frequency (p = 0.186) and the frequency span (p = 0.302).Scopu

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).Scopu

A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals

This paper establishes the relevance of the GePS measure for quantifying the global phase synchronization within multivariate nonstationary signals such as newborn EEG using an IP/IF estimation approach in the time-frequency (T-F) domain. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).This paper proposes a new approach to estimate the phase synchrony among nonstationary multivariate signals using the linear relationships between their instantaneous frequency (IF) laws. For cases where nonstationary signals are multi-component, a decomposition method like multi-channel empirical mode decomposition (MEMD) is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). We then apply the Johansen method on the IF laws to assess the phase synchrony within multivariate nonstationary signals. The proposed approach is validated first using multi-channel synthetic signals. The method is then used for quantifying the inter-hemispheric EEG asynchrony during ictal and inter-ictal periods using a newborn EEG seizure/non-seizure database of five subjects. For this application, pair-wise phase synchrony measures may not be able to account for phase interactions between multiple channels. Furthermore, the classical definition of phase synchrony, which is based on the rational relationships between phases, may not reveal the hidden phase interdependencies caused by irrational long-run relationships. We evaluate the performance of the proposed method using the differentiation of unwrapped phase as well as other IF estimation techniques. The results obtained on newborn EEG signals confirm that the generalized phase synchrony within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during inter-ictal periods

Detection of perinatal hypoxia using time-frequency analysis of heart rate variability signals

This paper presents a time-frequency approach to detect perinatal hypoxia by characterizing the nonstationary nature of heart rate variability (HRV) signals. Quadratic time-frequency distributions (TFDs) are used to represent the HRV signals. Six features based on the instantaneous frequency (IF) of the lower frequency components of HRV signals are selected to establish a classifier using support vector machine. The classifier is trained and tested using the signals recorded from a neonatal piglet model under a controlled hypoxic condition, which provides reliable annotations on the data. The method shows superior performance in the detection of hypoxic epochs with sensitivity (89.8%), specificity (100%) and total accuracy (94.9%) compared with that based on frequency domain features, indicating that nonstationarity should be taken into account for a more accurate assessment of the newborn status with possible hypoxia when analyzing HRV signals

Detection of perinatal hypoxia using time-frequency analysis of heart rate variability signals

This paper presents a time-frequency approach to detect perinatal hypoxia by characterizing the nonstationary nature of heart rate variability (HRV) signals. Quadratic time-frequency distributions (TFDs) are used to represent the HRV signals. Six features based on the instantaneous frequency (IF) of the lower frequency components of HRV signals are selected to establish a classifier using support vector machine. The classifier is trained and tested using the signals recorded from a neonatal piglet model under a controlled hypoxic condition, which provides reliable annotations on the data. The method shows superior performance in the detection of hypoxic epochs with sensitivity (89.8%), specificity (100%) and total accuracy (94.9%) compared with that based on frequency domain features, indicating that nonstationarity should be taken into account for a more accurate assessment of the newborn status with possible hypoxia when analyzing HRV signals.Scopu

Seizures are associated with brain injury severity in a neonatal model of hypoxia-ischemia

Hypoxia-ischemia is a significant cause of brain damage in the human newborn and can result in long-term neurodevelopmental disability. The loss of oxygen and glucose supply to the developing brain leads to excitotoxic neuronal cell damage and death; such over-excitation of nerve cells can also manifest as seizures. The newborn brain is highly susceptible to seizures although it is unclear what role they have in hypoxic-ischemic (H/I) injury. The aim of this study was to determine an association between seizures and severity of brain injury in a piglet model of perinatal H/I and, whether injury severity was related to type of seizure, i.e. sub-clinical (electrographic seizures only) or clinical (electrographic seizures+physical signs). Hypoxia (4% O(2)) was induced in anaesthetised newborn piglets for 30 min with a final 10 min period of hypotension; animals were recovered and survived to 72 h. Animals were monitored daily for seizures both visually and with electroencephalogram (EEG) recordings. Brain injury was assessed with magnetic resonance imaging (MRI), (1)H-MR spectroscopy ((1)H-MRS), EEG and by histology (haematoxylin and eosin). EEG seizures were observed in 75% of all H/I animals, 46% displayed clinical seizures and 29% sub-clinical seizures. Seizure animals showed significantly lower background amplitude EEG across all post-insult days. Presence of seizures was associated with lower cortical apparent diffusion coefficient (ADC) scores and changes in (1)H-MRS metabolite ratios at both 24 and 72 h post-insult. On post-mortem examination animals with seizures showed the greatest degree of neuropathological injury compared to animals without seizures. Furthermore, clinical seizure animals had significantly greater histological injury compared with sub-clinical seizure animals; this difference was not apparent on MRI or (1)H-MRS measures. In conclusion we report that both sub-clinical and clinical seizures are associated with increased severity of H/I injury in a term model of neonatal H/I

Performance evaluation of multi-component instantaneous frequency estimation techniques for heart rate variability analysis

Accurate instantaneous frequency (IF) estimation of the non-stationary heart rate signal is important in quantifying the heart rate variability (HRV) measures. This study compares the effectiveness of four IF estimation methods in analyzing HRV signals. Specifically, they are the direct localization of the maximal peaks in the signal time-frequency distribution (TFD), IF estimation based on component linking technique in the TFD, IF estimation using the TFD with optimal windows based on intersection of confidence intervals rule and complex demodulation. Results of applying the IF estimation methods to synthesized and real piglet HRV signals reveal that, the approach using component linking technique outperform the other techniques with respect to the accuracy and implementation. It provides new insights in studying the evolution of the autonomic nervous regulation of the cardiovascular function over time.Scopu

Passive detection of accelerometer-recorded fetal movements using a time–frequency signal processing approach

This paper presents a proof-of-concept that shows that the use of accelerometers can be used for the detection of Fetal Movements. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).This paper describes a multi-sensor fetal movement (FetMov) detection system based on a time–frequency (TF) signal processing approach. Fetal motor activity is clinically useful as a core aspect of fetal screening for well-being to reduce the current high incidence of fetal deaths in the world. FetMov are present in early gestation but become more complex and sustained as the fetus progresses through gestation. A decrease in FetMov is an important element to consider for the detection of fetal compromise. Current methods of FetMov detection include maternal perception, which is known to be inaccurate, and ultrasound imaging which is intrusive and costly. An alternative passive method for the detection of FetMov uses solid-state accelerometers, which are safe and inexpensive. This paper describes a digital signal processing (DSP) based experimental approach to the detection of FetMov from recorded accelerometer signals. The paper provides an overview of the significant measurement and signal processing challenges, followed by an approach that uses quadratic time–frequency distributions (TFDs) to appropriately deal with the non-stationary nature of the signals. The paper then describes a proof-of-concept with a solution consisting of a detection method that includes (1) a new experimental set-up, (2) an improved data acquisition procedure, and (3) a TF approach for the detection of FetMov including TF matching pursuit (TFMP) decomposition and TF matched filter (TFMF) based on high-resolution quadratic TFDs. Detailed suggestions for further refinement are provided with preliminary results to establish feasibility, and considerations for application to clinical practice are reviewed