By the same authors

Identifying usage anomalies for ECG-based sensor nodes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published copy (DOI)



Publication details

Title of host publicationIEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
DateE-pub ahead of print - 21 Jul 2016
DatePublished (current) - 2016
Number of pages6
Original languageEnglish
ISBN (Print)9781509030880

Publication series

NameWearable and Implantable Body Sensor Networks (BSN), IEEE International Conference on
ISSN (Electronic)2376-8894


Body Sensor Networks (BSNs) are being used across a wider range of applications including healthcare ones where sensors may be attached to the body to sense certain properties including Electrocardiogram (ECG). The dependability of the systems is a key concern and is affected by the way in which it is used. For example, if the leads are loosely attached then the resulting signal will not be useful. It has been reported that the rate of such error is around 4% in the intensive care unit [8] when operating medical devices by trained professionals. The problem is made worse as the users of the systems are often not trained professionals. Some work has been performed on detecting anomalous signals. However, all of it has concentrated on anomalies caused by medical conditions (e.g arrhythmia). That is, to the best of our knowledge, no prior work has looked at anomalies caused by incorrect usage. In this paper a range of usage anomalies are defined in conjunction with a cardiologist and a lightweight algorithm is developed that achieves a high identification rate.

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