Background and objectiveSignificant health care resources are allocated to monitoring high risk pregnancies to minimize growth compromise, reduce morbidity and prevent stillbirth. Fetal movement has been recognized as an important indicator of fetal health. Studies have shown that 25% of pregnancies with decreased fetal movement in the third trimester led to poor outcomes at birth. The studies have also shown that maternal perception of fetal movement is highly subjective and varies from person to person. A non-invasive system for fetal movement detection that can be used outside hospital would represent an advance in at-home monitoring of at-risk pregnancies. This is a challenging task that requires the use of advanced signal processing techniques to differentiate genuine fetal movements from contaminating artefacts.MethodsThis manuscript proposes a novel algorithm for automatic fetal movement recognition using data collected from wearable tri-axial accelerometers strategically placed on the maternal abdomen. The novelty of the work resides in the efficient removal of artefacts and in distinctive feature extraction. The proposed algorithm used independent component analysis (ICA) for dimensionality reduction and artefact removal. A supplemental technique based on discrete wavelet transform (DWT) was also used to remove artefacts.ResultsTo identify fetal movements, 31 features were extracted from the acceleration data. Based on these features, several classifiers were used to distinguish fetal from non-fetal movements. Robustness of the classifiers was tested for various concentrations of artefacts in the classification data. The best performance was achieved by Bagging classifier algorithm, with random forest as its basis classifier, yielding an accuracy ranging from 87.6% to 95.8% depending on the artefact concentration level.ConclusionsA high performance detection of fetal movements can be achieved using accelerometery-based systems suitable for long-term monitoring.

The Locally Optimized Spectrogram (LOS) defines a novel method for obtaining a high-resolution time–frequency (t,f) representation based on the short-time fractional Fourier transform (STFrFT). The key novelty of the LOS is that it automatically determines the locally optimal window parameters and fractional order (angle) for all signal components, leading to a high-resolution and cross-terms free time–frequency representation. This method is suitable for multicomponent and non-stationary signals without a priori signal information. Simulated signals, real biomedical applications, and various measures are used to validate the improved performance of the LOS and compare it with other state-of-the-art methods. The robustness of the LOS is also demonstrated under different signal-to-noise ratio (SNR) conditions. Finally, the relationship between the LOS and other time–frequency distributions (TFDs) is depicted and a recursive formulation is presented and shows the trade-off between the cross-terms suppression and auto-terms resolution.

Hypoxic-ischemic (HI) brain injury is a primary result of perinatal asphyxia (deficiency of oxygen and excess of carbon dioxide in the blood) and a significant contributor to poor neurodevelopment and severe neurological deficits in newborns. The cardiovascular response to hypoxia, which is important to prevent the core organs from the hypoxic insult, is variable between individuals, and the variability is associated with brain injury following hypoxia. It is hypothesized that the autonomic nervous system (ANS), especially the sympathetic branch (SNS), plays a determinant role in this variability. Understanding the physiological response to an HI insult may help with understanding the determinants of the subsequent neurodevelopmental outcome and with identifying fetuses and neonates who are at risk of HI brain injury. Another significant issue in perinatal research and clinical practice is how to accurately detect hypoxia, especially during the prenatal period when the fetus is not directly accessible. The features associated with the physiological response may improve the diagnosis of perinatal hypoxia. This thesis aims to: 1) Explore whether the SNS activity determines the inter-individual variation of the cardiovascular response to hypoxia and the subsequent neurological outcome; 2) Develop an accurate automated hypoxia detection method. Heart rate variability (HRV) signals are defined as the variation of inter-heartbeat intervals, and reflect the autonomic control of cardiovascular function. In this thesis, the time–frequency distribution (TFD) of HRV signals was used to measure the continuous autonomic response to hypoxia and to extract effective non-stationary features for the detection of perinatal hypoxia. A high resolution TFD is critical for the accuracy of autonomic measurement and feature extraction. Therefore, a class of HRV-adapted TFDs was proposed using modified lag-independent kernels. In these TFDs, a new parameter is defined as the minimal frequency difference among signal components, and superiorly removes the cross-terms and improves the TF resolution as compared with existing good-performing TFDs that commonly used in HRV analysis. The proposed TFD can also enhance the discriminating ability of TFD-based HRV features in hypoxia detection. To investigate the continuous SNS response to hypoxia in a neonatal piglet model with controlled hypoxia, the spectral power of the low frequency (LF) component of HRV signals was used to quantify the sympathetic regulation during hypoxia. The instantaneous component power was calculated based on the instantaneous frequency (IF) which was estimated using a component linking approach. The power of the LF component increased early during the period of hypoxia and ii then dropped to baseline levels prior to the heart rate (HR) and blood pressure (BP) reaching their maxima. There was no association between changes in the LF power and features of the cardiovascular response (HR pattern and BP index) to hypoxia or neurological outcome. These results suggest that the SNS contributes to the initial cardiovascular response to hypoxia in a stereotypical fashion but is then unable to maintain this contribution to the maintenance of adequate BP and organ perfusion. Therefore, the autonomic control (especially the sympathetic component) is not the source of inter-individual variation in the cardiovascular response to acute hypoxia since it is not the predominant factor in mediating the cardiovascular response to hypoxia. Nevertheless, HRV features do reflect the autonomic changes in response to hypoxia, and this provides useful information for the detection of perinatal hypoxia. Features useful for hypoxia detection including those based on the IF and instantaneous amplitude of HRV signal components as well as those derived from matrix decomposition of the signals’ time–frequency distributions using singular value decomposition and non-negative matrix factorization were explored. An automated detection approach was developed by feeding the selected features into a support vector machine classifier. The proposed method was tested using an accurate dataset recorded from a neonatal piglet model with accurately measured and controlled hypoxia and there was a strong detection performance with sensitivity 93.3%, specificity 98.3% and accuracy 95.8%. This method outperforms methods based on stationary features and those using non-stationary features.

Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time–frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals’ time–frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring.

The analysis of heart rate variability (HRV) provides a non-invasive tool for assessing the autonomic regulation of cardiovascular system. Quadratic time-frequency distributions (TFDs) have been used to account for the non-stationarity of HRV signals, but their performance is affected by cross-terms. This study presents an improved type of quadratic TFD with a lag-independent kernel (LIK-TFD) by introducing a new parameter defined as the minimal frequency distance among signal components. The resulting TFD with this LIK can effectively suppress the cross-terms while maintaining the time-frequency (TF) resolution needed for accurate characterization of HRV signals. Results of quantitative and qualitative tests on both simulated and real HRV signals show that the proposed LIK-TFDs outperform other TFDs commonly used in HRV analysis. The findings of the study indicate that these LIK-TFDs provide more reliable TF characterization of HRV signals for extracting new instantaneous frequency (IF) based clinically related features. These IF based measurements shown to be important in detecting perinatal hypoxic insult - a severe cause of morbidity and mortality in newborns. (C) 2013 Elsevier Ltd. All rights reserved.

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.

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.

To determine whether the sympathetic nervous system plays any role in the inter-individual variation of cardiovascular response to hypoxia in newborns, neonatal heart rate variability (HRV) analysis was used to assess autonomic response during hypoxia in piglets. Due to the nonstationary nature of HRV signals and the inaccuracy in pre-defined neonatal HRV frequency bands using standard spectral methods, we applied time-frequency distribution analysis to assess neonatal HRV. Although sympathetic activity was initially enhanced to stimulate the cardiac compensation, it did not account for the variation of cardiovascular response since the power of low frequency component in HRV neither showed corresponding changes with the heart rate nor had any correlation with the brain injury. This may be attributed to immaturity of the neural pathway.

In the electrocardiograph (ECG), R-wave is the positive upward deflection in the QRS complex which represents the depolarization of both left and right ventricles. Accurate detection of the R-wave peaks in the ECG plays a primary role in the construction and analysis of the heart rate variability (HRV). Numerous methods have been proposed to enhance the robustness and accuracy of the automatic detection. The majority of these methods have been developed for adult ECG and may not perform adequately in the case of the newborn. In this study, we analysed the performance of four R-wave detection methods that were applied on newborn piglet ECG data. These methods are based on: first derivative, wavelet transform, and nonlinear transform. The results of our performance analysis showed that the nonlinear approach based on the Hilbert transform marginally outperformed the others, with the highest sensitivity (Se) of 99.95%, the lowest detection error(ER) of 0.12% and a high positive prediction (+P) of 99.93%.

We compute directly the entanglement entropy of spatial regions in Chern-Simons gauge theories in 2+1 dimensions using surgery. We use these results to determine the universal topological piece of the entanglement entropy for Abelian and non-Abelian quantum Hall fluids.

Today, China has become the world's second largest pollution source of CO2. Owing to coal-based energy consumption, it is estimated that 85-90% of the SO2 and CO2 emission of China results from coal rise. With high economic growth and increasing environmental concerns, China's energy consumption in the next few decades has become an issue of active concern. Forecasting of energy demand over long periods, however, is getting more complex and uncertain. It is believed that the economic and energy systems are chaotic and nonlinear. Traditional lineal system modeling, used mostly in energy demand forecasts, therefore, is not a useful approach. In view of uncertainty and imperfect information about future economic growth and energy development, an uncertain dynamic system model, which has the ability to incorporate and absorb the nature of an uncertain system with imperfect or incomplete information, is developed. Using the model, the forecasting of energy demand in the next 25 years is provided. The model predicts that China's energy demand in 2020 will be about 2,700-3,000 Mtce, coal demand 3,500 Mt, increasing by 128% and 154%, respectively, compared with that of 1995.