DMSense: A non-invasive Diabetes Mellitus Classification System using Photoplethysmogram signal

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) Pub Date : 2017-03-13 DOI:10.1109/PERCOMW.2017.7917526

V. R. Reddy, A. Choudhury, Parijat Deshpande, Srinivasan Jayaraman, N. Thokala, Venkatesh Kaliaperumal

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引用次数: 12

Abstract

The alarming statistics of Diabetes Mellitus (DM) Type 2 as the most common and prevalent disease in India and world over [1] has fuelled research in the direction of non-invasive and continuous monitoring of this disease. This paper describes a demonstration of an inexpensive mobile-phone based android application which can collect Photoplethysmogram (PPG) from fingertip via built-in camera and flash and transfer it to a high-end cloud server for early detection of DM. Additionally, this application allows continuous monitoring of DM patients can aid in assisting the short and long-term complication risks. The proposed application is targeted to cater to the inherent demand to for a mobile-based, pervasive system for continuous, non-invasive monitoring and detection of DM. Our application has been successfully deployed on Nexus 5 and tested on controlled and diabetic group with 80% specificity and 84% sensitivity for a 100 patient dataset and presented in this paper.

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DMSense:一种利用光容积图信号的无创糖尿病分类系统

2型糖尿病(DM)是印度和世界上最常见和流行的疾病[1]，这一令人震惊的统计数据推动了对该疾病的非侵入性和连续监测方向的研究。本文介绍了一种廉价的基于手机的android应用程序的演示，该应用程序可以通过内置摄像头和闪光灯从指尖收集Photoplethysmogram (PPG)，并将其传输到高端云服务器，用于DM的早期检测。此外，该应用程序允许对DM患者进行持续监测，有助于协助短期和长期并发症风险。提出的应用程序旨在满足对基于移动的、普及的系统的固有需求，用于连续、无创地监测和检测糖尿病。我们的应用程序已成功部署在Nexus 5上，并在100例患者数据集中对控制和糖尿病组进行了80%特异性和84%敏感性的测试，并在本文中提出。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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