Abstract
In recent years, there has been a growing interest in smart e-Health systems to improve people’s quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of diseases like Arrhythmia at its early onset that otherwise may become fatal if not detected on time. In this work, we developed a cognitive dynamic system (CDS)-based framework for the smart e-Health system to realize an automatic screening process in the presence of a defective or abnormal dataset. A defective dataset may have poor labeling and/or lack enough training patterns. To mitigate the adverse effect of such a defective dataset, we developed a decision-making system that is inspired by the decision-making processes in humans in case of conflict-of-opinions (CoO). We present a proof-of-concept implementation of this framework to automatically identify people having Arrhythmia from single lead Electrocardiogram (ECG) traces. It is shown that the proposed CDS performs well with the diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, being in good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, respectively. The proposed CDS algorithm can be incorporated in the autonomic computing layer of a smart-e-Health-home platform to achieve a pre-defined degree of screening accuracy in the presence of a defective dataset.
Highlights
The autonomic decision-making systems (ADMS) [1,2,3] for smart interactive cyber-physical systems (CPS) are attracting much attention from researchers and technology providers [1,2,3,4,5,6,7]
The cognitive dynamic system (CDS) is inspired by the neuroscience model of the human brain presented in [8] and it is built on the principles of cognition, i.e., perception-action cycle (PAC), memory, attention, intelligence, and language [9,10,11,12,13,14,15,16]
We present a cognitive dynamic system (CDS) for the screening process in smart e-Health systems based on the perception and multiple action cycles (PMAC) and the decision-making processes in humans in case of a conflict of opinion (CoO)
Summary
The autonomic decision-making systems (ADMS) [1,2,3] for smart interactive cyber-physical systems (CPS) are attracting much attention from researchers and technology providers [1,2,3,4,5,6,7]. When the datasets are not reliable due to poor labeling and/or insufficient training patterns defective or abnormal, the PAC-based CDS cannot perform well enough to satisfy requirements to provide reliable results for predefined healthcare policy This can be explained with an analogy to the decision-making process of the human brain when it makes a judgment based on some ambiguous information, running a risk of making a wrong decision. It is shown that the proposed CDS performs well, giving good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, achieving average final diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, respectively These diagnosis errors correspond to a clinically acceptable false alarm rates [19] of 20.1%, 25%, 28.4%, and 54.7% respectively, even with a defective dataset
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