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Design and Implementation of an Ultralow-Power Ecg Patch and Smart Cloud-Based Platform Publisher



Baraeinejad B1 ; Shayan MF1 ; Vazifeh AR1 ; Rashidi D2 ; Hamedani MS3 ; Tavolinejad H4 ; Gorji P1 ; Razmara P1 ; Vaziri K5 ; Vashaee D6 ; Fakharzadeh M5
Authors
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Authors Affiliations
  1. 1. Biosen Group, Sharif Technology Service Complex, Sharif University of Technology, Tehran, Iran
  2. 2. Mathematics Department, Alzahra University, Tehran, 1985717443, Iran
  3. 3. School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 1411713138, Iran
  4. 4. Department of Cardiac Electrophysiology, Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, 1458889694, Iran
  5. 5. Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
  6. 6. Electrical and Computer Engineering Department, North Carolina State University, Raleigh, 27606, NC, United States

Source: IEEE Transactions on Instrumentation and Measurement Published:2022


Abstract

This article reports the development of a new smart electrocardiogram (ECG) monitoring system, consisting of the related hardware, firmware, and Internet of Things (IoT)-based web service for artificial intelligence (AI)-assisted arrhythmia detection and a complementary Android application for data streaming. The hardware aspect of this article proposes an ultralow power patch sampling ECG data at 256 samples/s with 16-bit resolution. The battery life of the device is two weeks per charging, which alongside the flexible and slim (193.7 mm times62.4 mm times8.6 mm) and lightweight (43 g) allows the user to continue real-life activities while the real-time monitoring is being done without interruption. The power management is achieved through the usage of switching converters, ultralow power component choice, as well as intermittent usage of them through firmware optimization. A novel data encoding method is also proposed to allow the compression of data and lower the runtime. The software aspect, in addition to the web ECG analysis platform and the Android streaming and monitoring application, provides an arrhythmia detection service. The key innovations in this regard are the usage of a set of new factors in determining arrhythmia that grants higher accuracy while retaining the detection near-real-time. The arrhythmia detection algorithm shows 98.7% accuracy using artificial neural network and K-nearest neighbors methods and 98.1% using decision tree method on test dataset. © 1963-2012 IEEE.
Related Docs
1. Low-Power Online Ecg Analysis Using Neural Networks, Proceedings - 19th Euromicro Conference on Digital System Design# DSD 2016 (2016)
2. Mobile Cardiac Health-Care Monitoring and Notification With Real Time Tachycardia and Bradycardia Arrhythmia Detection, Journal of Medical Signals and Sensors (2017)