Natural Brain-Inspired Intelligence for Screening in Healthcare Applications

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.

Keywords

• Autonomic decision-making system
• autonomic computing layer
• cognitive dynamic system (CDS)
• cognitive decision making (CDM)
• non-Gaussian and non-linear environment
• NGNLE