Fei He

Coventry University

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Ptolemaios G. Sarrigiannis

Royal Devon & Exeter NHS Foundation Trust

Min Wu

Institute for Infocomm Research

D. Blackburn

University of Sheffield

Dominik Klepl

Coventry University

Stephan Goerttler

Coventry University

Hua‐Liang Wei

University of Sheffield

Martin Brown

Kennesaw State University

Yifan Zhao

Cranfield University

S.A. Billings

Jacksonville College

Zefeng Li

Chinese Academy of Medical Sciences & Peking Union Medical College

Cooperative Institutions

Institute for Health Metrics and Evaluation

399

University of Washington

363

Seattle University

128

Tehran University of Medical Sciences

Auckland University of Technology

University College London

University of Sydney

University of Groningen

Sapienza University of Rome

University of Queensland

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Case report: New clinical clue of hyper-endogenous heparinization provides an imperative guideline for Treatment of Postpartum Hemorrhage with DIC via TEG guides and protamine intervention

Research Square (Research Square) (2020)

Yan Tian Fei He Dong Lai Ji Changfu

Abstract Background Postpartum hemorrhage (PPH) is a leading cause of maternal mortality and severe maternal morbidity worldwide and involves disseminated intravascular coagulation (DIC) secondary to cause massive blood loss. The coagulation abnormality in response to severe trauma or infection is a latent cause that may aggravate PPH. Case presentation A 26-year-old puerpera with 39 weeks of menolipsis case lacks amniotic fluid and uterus infection was examined. During the cesarean section, the patient presented fever and massive hemorrhage that she became the DIC and shock. The low coagulation of this PPH patient was diagnosed by Thromboelastography (TEG) guiding with heparinase (type I). The patient appeared a critical condition admitted in emergency room. According the continuous detection via TEG guides assay, she was observed in coagulopathy and hyper-heparinization. And subsequently the protamine correction for the patient's coagulation abnormality, the patient who became a stable condition after 4 hours of urgent treatment. Conclusion TEG-guided determination of endogenous heparin and subsequent infusion of protamine effectively reversed the syndrome of PPH with DIC. This is the first case report that infection and bleeding might be major causes of hyper-endogenous heparinization without exogenous heparin intervention. The hyper-endogenous heparinization is clinically taken into consideration for the syndrome of PPH with low coagulation.

Thromboelastography

Coagulation testing

Coagulation Disorder

10.21203/rs.3.rs-35288/v1

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EEG Signal Processing Techniques and Applications

Sensors (2023)

Yifan Zhao Fei He Yuzhu Guo

Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...].

SIGNAL (programming language)

EEG-fMRI

10.3390/s23229056

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Causality Analysis with Information Geometry: A Comparison

Entropy (2023)

H. Choong Eun‐jin Kim Fei He

The quantification of causality is vital for understanding various important phenomena in nature and laboratories, such as brain networks, environmental dynamics, and pathologies. The two most widely used methods for measuring causality are Granger Causality (GC) and Transfer Entropy (TE), which rely on measuring the improvement in the prediction of one process based on the knowledge of another process at an earlier time. However, they have their own limitations, e.g., in applications to nonlinear, non-stationary data, or non-parametric models. In this study, we propose an alternative approach to quantify causality through information geometry that overcomes such limitations. Specifically, based on the information rate that measures the rate of change of the time-dependent distribution, we develop a model-free approach called information rate causality that captures the occurrence of the causality based on the change in the distribution of one process caused by another. This measurement is suitable for analyzing numerically generated non-stationary, nonlinear data. The latter are generated by simulating different types of discrete autoregressive models which contain linear and nonlinear interactions in unidirectional and bidirectional time-series signals. Our results show that information rate causalitycan capture the coupling of both linear and nonlinear data better than GC and TE in the several examples explored in the paper.

Causality

10.3390/e25050806

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Comparing Spatial and Spectral Graph Filtering for Preprocessing Neurophysiological Signals

Stephan Goerttler Fei He Min Wu D. Blackburn Ptolemaios G. Sarrigiannis

Signals measured with multiple sensors simultaneously in time form multivariate signals and are commonly acquired in biomedical imaging. These temporal signals are generally not independent of each other, but exhibit a rich spatial structure. Graph filtering, either spatial or spectral, is a method that can leverage this spatial structure for various preprocessing tasks, such as graph denoising. Previous studies have focused on learning the parameters of spatial graph impulse response (GIR) filters, while neglecting spectral graph frequency response (GFR) filters, even though GFR filters offer unique advantages in terms of regularisation and interpretation. In this study, we therefore compare learning GIR filters and GFR filters as a trainable preprocessing step for two different neural networks on an Alzheimer's classification task. We tested both a functional connectivity graph as well as a geometric graph as the base of each filter type, and varied the localisation of the spatial filter. As expected, the retrieved shapes of the trained filters suggest that GFR filters can be interpreted in terms of their graph structure, while the same does not hold for GIR filters. Contrarily, however, we found that only the geometric, highly localised GIR filter outperforms the baseline significantly, surpassing it by 3.8 percentage points. These findings suggest that the observed performance boost of a trained localised GIR filter may in fact not be due to the graph structure. Instead, we hypothesise that this boost is caused by favourable algebraic properties of the filter matrix.

Neurophysiology

Spectral Analysis

10.1109/spmb59478.2023.10372719

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Red, Blue or Mix: Choice of Optimal Light Qualities for Enhanced Plant Growth and Development through in silico Analysis

in silico Plants (2024)

Andrew Ming Him Chan Miao Lin Pay Jesper Christensen Fei He Laura C. Roden

Abstract In smart greenhouse farming, the impact of light qualities on plant growth and development is crucial but lacks systematic identification of optimal combinations. This study addresses this gap by analysing various light properties’ effects (photoperiod, intensity, ratio, light–dark order) on Arabidopsis thaliana growth using days-to-flower (DTF) and hypocotyl length as proxies to measure plant growth and development. After establishing suitable ranges through a comprehensive literature review, these properties varied within those ranges. Compared to white light, a 16-h cycle of blue light reduces DTF and hypocotyl length by 12 % and 3 %, respectively. Interestingly, similar results can be achieved using a shorter photoperiod of 14-h light (composed of 8 h of a mixture of 66.7 μmol m−2s−1 red and 800 μmol m−2s−1 blue lights (i.e. blue:red ratio of 12:1) followed by 6 h of monochromatic red light and 10-h dark. These findings offer potential for efficient growth light recipes in smart greenhouse farming, optimizing productivity while minimizing energy consumption.

10.1093/insilicoplants/diae008

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Discrete-Time Model Representation for Biochemical Pathway Systems

Fei He L.F. Yeung Martin Brown

This paper presents the importance of formulating general discrete-time model representations for current pathway deterministic modeling study. Discrete-time models can be considered as a link between continuous-time kinetic reactions and discrete-time experimentation as well as computer based simulation and analysis. In the paper, different discretization techniques are investigated according to different sorts of ODE model structures. Two discretization strategies are mainly focused, that are one-step Taylor/Lie series based method and multi-step Runge-Kutta method. A new discretization approach based on Taylor expansion and Carleman linearization is proposed for bilinear in states pathway models. Finally, the superiority of using Runge-Kutta based approach as general discrete-time model representations are concluded.

Ode

Linearization

Representation

Discrete modelling

Runge–Kutta methods

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Retrieving Filter Spectra in CNN for Explainable Sleep Stage Classification

arXiv (Cornell University) (2025)

Stephan Goerttler Yucheng Wang Emadeldeen Eldele Fei He Min Wu

Despite significant advances in deep learning-based sleep stage classification, the clinical adoption of automatic classification models remains slow. One key challenge is the lack of explainability, as many models function as black boxes with millions of parameters. In response, recent work has increasingly focussed on enhancing model explainability. This study contributes to these efforts by globally explaining spectral processing of individual EEG channels. Specifically, we introduce a method to retrieve the filter spectrum of low-level convolutional feature extraction and compare it with the classification-relevant spectral information in the data. We evaluate our approach on the MSA-CNN model using the ISRUC-S3 and Sleep-EDF-20 datasets. Our findings show that spectral processing plays a significant role in the lower frequency bands. In addition, comparing the correlation between filter spectrum and data-based spectral information with univariate performance indicates that the model naturally prioritises the most informative channels in a multimodal setting. We specify how these insights can be leveraged to enhance model performance. The code for the filter spectrum retrieval and its analysis is available at https://github.com/sgoerttler/MSA-CNN.

Sleep

10.48550/arxiv.2502.06478

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On the Dying Out of the Spanish Muslim

Fei He

The expelling of Muslim from Spain ultimately solved the problem of reconquering, which lasted more than 800 years, and meanwhile Muslim culture dropped its curtain in Iberia peninsula. This large -scale expelling disseminated a deep hidden danger for Spanish Imperial' s decline.

Peninsula

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PI3K/Aktシグナル伝達経路に基づく喘息ラットTreg細胞に対するシャクヤク甘草湯の作用を検討した。【JST・京大機械翻訳】

Ru Chuhui Fei He Qi Zheng Ji Yemin Xu Jianpu

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Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals

Entropy (2024)

Jia-Chen Hua Eun‐jin Kim Fei He

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal's instantaneous influence on another signal's information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer's disease as presented in this work.

Information Theory

10.3390/e26030213

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