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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (12), p.2733
• Main Authors: Ukil, Arijit, Jara, Antonio J, Marin, Leandro
• Format: Article
• Language: English
• Subjects: Algorithms; Analytics; anomaly detection; Artificial intelligence; Automation; Biosensing Techniques; cardiac heart monitoring; Caregivers; Computer Security; differential privacy; edge analytics; Health care; Heart - physiology; Hospitals; Humans; Integrated approach; International conferences; Internet of Things; IoT; Machine Learning; Monitoring, Physiologic - methods; Physicians; Privacy; Prospective Studies; Researchers; Sensors; Signal processing; Signal Processing, Computer-Assisted; Smartphones
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19122733

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.