Identifying usage anomalies for ECG-based sensor nodes

Abstract

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.