Abstract:
In this paper the application of statistical approach for anomaly detection in a wearable ECG monitor has been considered. The method is based on the usage of the pre-def...Show MoreMetadata
Abstract:
In this paper the application of statistical approach for anomaly detection in a wearable ECG monitor has been considered. The method is based on the usage of the pre-defined expected behavior of the monitored biomedical signal and its on-line comparison with real-time measurements. Such comparison can be implemented based on the statistical model of ECG signal. To test the accuracy of the proposed method a ProSim 8 simulator was used to generate ECG signals. Our experiments showed that critical anomalies in ECG, such as Cardiac Failure, different types of Arrhythmia, and ST-segment deviations can be detected with high precision (96~100%), while false positive rate over typical NSR signal is low (<;4%). The method is scalable in terms of required performance, power consumption, and the precision of anomaly detection to be suitable for the implementation in a wearable platform.
Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 16-20 August 2016
Date Added to IEEE Xplore: 18 October 2016
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PubMed ID: 28324926