Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform

Background Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties. Methods We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features. Results We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features. Conclusions SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

Geohazard Investigation On Gas Cloud Distribution At ‘B’ Field (Channel Influence)

Sedimentary rock deposition occur in very fast rates in offshore basin and might cause shallow subsurface geohazards that will incur high risk and increase cost of drilling operations. In general, offshore geohazards consist of a variety of geological features that contribute potential risks to the labour force, offshore amenities including the environment and surrounding areas due to the consequences of long or short period of geological processes. Therefore, further study need to be done properly in terms of geohazards classification that is significant to the offshore oil and gas developments in the Malay Basin (Bujang Field, refer Figure 1); such as shallow gas, gas hydrate, shallow water flow, slumping, landslides, faulting, pockmarks and liquefaction. To mitigate the point of costly drilling and safety risks, several techniques are needed during data gathering to visualize, interpret and identify the potential shallow drilling hazards. Besides, to a geoscientist, data integration and modelling techniques can be used to analyse the structural and physical circumstances of shallow subsurface. At the same time, gas models and geohazards map can be established based on seabed hazard analysis from seismic data to plan secure wells. Several seismic attributes such as instantaneous phase, instantaneous frequency, remove bias and envelope (reflection strength) had been used for channel detection. For gas cloud identification, seismic attributes such as remove bias, instantaneous phase, Chaos and RMS (Root Mean Square) amplitude are used. Besides that, spectral decomposition technique are used to display channel systems and other stratigraphic features in the field. Generally, this paper will explain about the meaning of geohazards in the oil and gas industry, the types of geohazards, general geohazards analysis, and will focuss on the identification of gas cloud through channel structure by applying several seismic attributes on specific parameters. All of this will be related to geohazards perspective and consequently, precautions can be undertaken systematically.

A closed-loop stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for closed-loop sensory stimulation in electroencephalography (EEG) experiments. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. We demonstrate that the performance of our method shows higher accuracy in predicting the EEG phase than the conventional autoregressive model-based method. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated autoregressive model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

EEG—Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction

This paper tackles the complex issue of detecting and classifying epileptic seizures whilst maintaining the total calculations at a minimum. Where many systems depend on the coupling between multiple sources, leading to hundreds of combinations of electrodes, our method calculates the instantaneous phase between non-identical upper and lower envelopes of a single-electroencephalography channel reducing the workload to the total number of electrode points. From over 600 h of simulations, our method shows a sensitivity and specificity of 100% for high false-positive rates and 83% and 75%, respectively, for moderate to low false positive rates, which compares well to both single- and multi-channel-based methods. Furthermore, pre-ictal variations in synchronisation were detected in over 90% of patients implying a possible prediction system.

Vibration-based synchronous sampling and its application in wind-turbine drive-train-condition monitoring

Utilizing shaft-speed information to analyse vibration signals is an important method for fault diagnosis and condition monitoring of rotating machineries, especially for those running at variable speeds. However, in many cases, shaft-speed information is not always available, for a variety of reasons. Fortunately, in most of the measurements, the shaft-speed information is embedded in the vibration response in many different forms, such as in the format of the fundamental shaft-rotation-frequency response and its harmonics, and the gear-meshing-frequency response and its harmonics, etc. Proper signal processing can be used to extract the shaft instantaneous speed from the measured vibration responses. In existing instantaneous shaft-speed-identification methods, a narrow-bandpass filtering technique is used explicitly or implicitly. In a complex gearbox system, such as that used in a wind turbine, the gear-meshing-response component could be modulated by many other shaft speeds, due to the configuration of the gearbox or due to the existence of component damage. As a result, it is very difficult to isolate a single vibration-response component for instantaneous shaft-speed detection. In this paper, an innovative approach is presented. The instantaneous shaft speed is extracted based on maxima tracking from the vibration-response spectrogram. A numerical integration scheme is employed to obtain the shaft instantaneous phase. Digital-domain synchronous resampling is then applied to the vibration data by using the instantaneous phase information. Due to the nature of noise suppression in the numerical integration, the accuracy of synchronous sampling is greatly improved. This proposed approach demonstrates the feasibility and engineering applicability through a controlled laboratory test case and two field wind-turbine cases. More detailed results and conclusions of this research are presented at the end of this paper.