Browsing by Author "He, Fei"

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    A collaborative machine tool maintenance planning system based on content management technologies
    (Springer, 2016-12-03) Wan, Shan; Li, Dongbo; Gao, James X.; Roy, Rajkumar; He, Fei
    From product maintenance and service point of view, high-value sophisticated computer numerical control (CNC) machine tools in modern manufacturing factories play important roles: they are manufacturing equipment, and on the other hand, they are also products supplied by equipment manufacturers. There is a trend that manufacturers are extending their responsibilities to the products use phase to meet customers’ requirements for lifetime support and service. To ensure the effective performance and efficient maintenance of high-value machine tools, information and knowledge from their lifecycle should be collected and reused. However, in the research area of product service systems and related computerised maintenance systems, there is a lack of research work on how to integrate knowledge from different stakeholders into the maintenance and service planning process, which is important for modern digital manufacturing systems to reduce machine tools’ downtime and improve their working performance. This project proposed a collaborative maintenance planning framework to connect different stakeholders and integrate their knowledge into the maintenance and service process. The potential of advanced content management systems (CMSs), which are widely used non-engineering sectors such as finance, business, publishing and government organisations, has been explored and tested for applications in the manufacturing engineering domain. The research realised that CMSs have several advantages compared with traditional engineering information systems, especially in managing dynamic and unstructured knowledge. A prototype maintenance and service planning system has been developed and evaluated using a real CNC machine tool.
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    The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings
    (Elsevier, 2018-01-08) Sarrigiannis, Ptolemaios G.; Zhao, Yifan; He, Fei; Billings, Stephen A.; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hua-Liang; Hadjivassiliou, Marios; Grünewald, Richard
    Objective To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). Methods We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. Results A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of “isochronous” (0-2ms) synchronisation but dynamic time lag changes occur during SW discharges. Conclusions In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Significance Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory.
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    EEG signal processing techniques and applications
    (MDPI, 2023-11-09) Zhao, Yifan; He, Fei; Guo, Yuzhu
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    EEG signal processing techniques and applications—2nd Edition
    (MDPI, 2025-02-01) Wei, Hua-Liang; Guo, Yuzhu; He, Fei; Zhao, Yifan
    Electroencephalography (EEG), as a well-established, non-invasive tool, has been successfully applied to a wide range of conditions due to its many evident advantages, such as economy, portability, easy operation, easy accessibility, and widespread availability in hospitals. EEG signals, with ultra-high time resolution, are vital in understanding brain functions. Traditionally, considerable attention in EEG signal processing and analysis has been paid to understanding brain activities from various perspectives, such as the detection and identification of abnormal frequencies in specific biological states, spatial–temporal and morphological characteristics of neurological disorder behaviours (e.g., paroxysmal or persistent discharges), the response of the brain nervous/neurological system to external stimuli, and the effects and responses to intermittent photic stimulation [1].
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    A pilot study investigating a novel non-linear measure of eyes open versus eyes closed EEG synchronization in people with Alzheimer's disease and healthy controls
    (MDPI, 2018-07-17) Blackburn, Daniel J.; Zhao, Yifan; De Marco, Matteo; Bell, Simon M.; He, Fei; Wei, Hua-Liang; Lawrence, Sarah; Unwin, Zoe C.; Blyth, Michelle; Angel, Jenna; Baster, Kathleen; Farrow, Thomas F. D.; Wilkinson, Iain D.; Billings, Stephen A.; Venneri, Annalena; Sarrigiannis, Ptolemaios G.
    Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.
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    The role of wrist-worn technology in the management of Parkinson’s disease in daily life: a narrative review
    (Frontiers, 2023-04-12) Li, Peng; van Wezel, Richard; He, Fei; Zhao, Yifan; Wang, Ying
    Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.