2022 journal article

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


By: B. Baraeinejad*, M. Shayan*, A. Vazifeh*, D. Rashidi*, M. Hamedani*, H. Tavolinejad*, P. Gorji*, P. Razmara* ...

author keywords: Arrhythmia detection; artificial intelligence (AI); cardiovascular diseases (CVD); cloud storage; electrocardiogram (ECG); Internet of Things (IoT); wearable sensors
Source: Web Of Science
Added: May 2, 2022

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times62.4$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times8.6$ </tex-math></inline-formula> 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.