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Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking

1
Research and Innovation, Tata Consultancy Services, Kolkata 700156, India
2
Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland
3
HOP Ubiquitous, 30562 Murcia, Spain
4
Area of Applied Mathematics, Department of Engineering and Technology of Computers, Faculty of Computer Science, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(12), 2733; https://doi.org/10.3390/s19122733
Received: 8 May 2019 / Revised: 12 June 2019 / Accepted: 13 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue Recent Advances in Fog/Edge Computing in Internet of Things)
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Abstract

Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk. View Full-Text
Keywords: IoT; cardiac heart monitoring; edge analytics; anomaly detection; differential privacy IoT; cardiac heart monitoring; edge analytics; anomaly detection; differential privacy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ukil, A.; Jara, A.J.; Marin, L. Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking. Sensors 2019, 19, 2733.

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