Sensing

A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.

2 column colour block - Research areas

Body Sensor Networks

Body Sensor Networks

The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for healthcare and the use of wireless wearable devices for clinical applications. One key development in this area is implantable in vivo monitoring and intervention devices.
Surgical Implants

Surgical Implants

One of the major focuses of the Hamlyn Centre is the development of miniaturised sensors embodied either as smart surgical appliances or implants to enhance the safety of surgical procedures, enhance patient care, and also the patient's quality of life.

2 column colour block - Research areas 2

ESPRIT

IoT and Smart Environments

Our main research in IoT and smart environments is focussed on the challenges of ambient sensing through the introduction of novel sensing platforms, self-configuration approaches, self-adaptive techniques, and seamless integration with wearable sensors.
Smart Environments

ESPRIT

The vision of ESPRIT is to position UK at the forefront of pervasive sensing in elite sports and promote its wider application in public life-long health, wellbeing and healthcare. Our work addresses some of the key challenges related to miniaturised wearable sensors for sports.

Citation

BibTex format

@article{Zhang:2020:10.1109/JBHI.2019.2933046,
author = {Zhang, Y and Guo, Y and Yang, P and Chen, W and Lo, B},
doi = {10.1109/JBHI.2019.2933046},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {465--474},
title = {Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network},
url = {http://dx.doi.org/10.1109/JBHI.2019.2933046},
volume = {24},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial preictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.
AU  - Zhang,Y
AU  - Guo,Y
AU  - Yang,P
AU  - Chen,W
AU  - Lo,B
DO  - 10.1109/JBHI.2019.2933046
EP  - 474
PY  - 2020///
SN  - 2168-2194
SP  - 465
TI  - Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2019.2933046
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000516606600015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/8788635
UR  - http://hdl.handle.net/10044/1/88047
VL  - 24
ER  -
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