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.


BibTex format

author = {Zhang, Y and Zhang, Y and Lo, B and Xu, W},
doi = {10.1111/exsy.12432},
journal = {Expert Systems},
pages = {1--13},
title = {Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASEbased feature selection},
url = {http://dx.doi.org/10.1111/exsy.12432},
volume = {37},
year = {2020}

RIS format (EndNote, RefMan)

AB - Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of lowcost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, KNearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.
AU - Zhang,Y
AU - Zhang,Y
AU - Lo,B
AU - Xu,W
DO - 10.1111/exsy.12432
EP - 13
PY - 2020///
SN - 0266-4720
SP - 1
TI - Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASEbased feature selection
T2 - Expert Systems
UR - http://dx.doi.org/10.1111/exsy.12432
UR - https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.12432
UR - http://hdl.handle.net/10044/1/75184
VL - 37
ER -