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

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.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…The main tool used to pick up signals was the digital stethoscope, mentioning in 44 articles (IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,44,45,46,48,49,50,51,52,53,54,56). The connectivity of digital stethoscopes occurs, in most equipment, by Bluetooth, not over the Internet, and for this reason these equipments are not considered IoT devices.…”

“…Among the 58 articles selected, 34 works (58.62%,IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

“…Ren et al [42] (2) used convolutional neural networks (CNN) (19 articles cite CNN, IDs = 1, 2, 10, 19,23,31,32,33,39,41,43,46,47,49,51,52,54,55,58) for classification of PCG scale images for the task of classifying cardiac sounds between normal and abnormal. PCG signal representations were obtained by dataset from: Massachusetts Institute of Technology (MIT), Aalborg University, Aristotle University of Thessaloniki, University of Haute Alsace, Dalian University of Technology, and Shiraz University.…”

Privacy-preserving chi-squared test of independence for small samples

“…The most influential privacy metric within the privacy community is -differential privacy [22], which has been intensively investigated [23][24][25][26]. Several researchers, such as Fienberg et al [27], Uhlerop et al [28], and Yu et al [7], have suggested approaches to facilitate sharing of χ 2 values while conforming with -differential privacy parameters.…”

On the Knowledge-Driven Analytics and Systems Impacting Human Quality of Life

“…One of the main challenges in such applications is to protect the sensitive information [3 -4]. In fact, it is demonstrated that automation of cardiac management is plausible under privacy preserving derisking scheme [5]. Privacy along with data security are the main concerns that human-centric systems and application need to concentrate as such systems invariably collect, analyze and infer on human-related information and such information is sensitive in nature [6][7][8].…”