Abstract
Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.
Highlights
Cardiovascular diseases are worldwide considered as one of the major causes of death [1]
Our main contributions are: 1. We propose a fuzzy logic based confidence metric for the quality assessment of the calculated Early Warning Score (EWS), 2. we show how a fuzzy logic based reliability metric gives information about the correctness of the input data, 3. we introduce a method for combing the input data reliability and the confidence of the system to calculate output data reliability based on both factors, and
To evaluate which of these signals are either correct or are containing errors, the output of the conventional EWS system processing an experiment was compared with the EWS Ground Truth Dataset (EGTDS) of the same experiment
Summary
Cardiovascular diseases are worldwide considered as one of the major causes of death [1]. Internet of Things (IoT) - with its small devices and wearable technologies - is a key enabler to provide autonomous health monitoring for a mobile EWS system in a cost-efficient manner [4,5,6,7]. Such a system cannot be supervised continuously by healthcare professionals, but its reliability and the accuracy of the calculated EWS are of utter importance. In one of our previous works [13], Mobile Netw Appl we already presented a data reliability assessment technique based on fuzzy logic, which gives information about the trustfulness of the calculated EWS.
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