Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices
MO Agyeman, AF Guerrero, QT Vien - IEEE access, 2022 - ieeexplore.ieee.org
The rise of Telemedicine has revolutionized how patients are being treated, leading to
several advantages such as enhanced health analysis tools, accessible remote healthcare …
several advantages such as enhanced health analysis tools, accessible remote healthcare …
ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices
A Amirshahi, M Hashemi - IEEE transactions on biomedical …, 2019 - ieeexplore.ieee.org
This paper presents a novel ECG classification algorithm for inclusion as part of real-time
cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is …
cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is …
[HTML][HTML] A multi-variate heart disease optimization and recognition framework
Cardiovascular diseases (CVD) are the most widely spread diseases all over the world
among the common chronic diseases. CVD represents one of the main causes of morbidity …
among the common chronic diseases. CVD represents one of the main causes of morbidity …
ECG_SegNet: An ECG delineation model based on the encoder-decoder structure
X Liang, L Li, Y Liu, D Chen, X Wang, S Hu… - Computers in biology …, 2022 - Elsevier
With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is
necessary to develop models and algorithms that can analyze the large amounts of ECG …
necessary to develop models and algorithms that can analyze the large amounts of ECG …
[PDF][PDF] A hybrid lightweight 1D CNN-LSTM architecture for automated ECG beat-wise classification.
Y Obeidat, AM Alqudah - Traitement du Signal, 2021 - researchgate.net
Accepted: 26 September 2021 In this paper we have utilized a hybrid lightweight 1D deep
learning model that combines convolutional neural network (CNN) and long short-term …
learning model that combines convolutional neural network (CNN) and long short-term …
HeartNet: Self multihead attention mechanism via convolutional network with adversarial data synthesis for ECG-based arrhythmia classification
Cardiovascular disease is now one of the leading causes of morbidity and mortality.
Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular …
Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular …
[HTML][HTML] NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications
Highly accurate classification methods for multi-task biomedical signal processing are
reported, including neural networks. However, reported works are computationally …
reported, including neural networks. However, reported works are computationally …
[HTML][HTML] An ECG stitching scheme for driver arrhythmia classification based on deep learning
DH Kim, G Lee, SH Kim - Sensors, 2023 - mdpi.com
This study proposes an electrocardiogram (ECG) signal stitching scheme to detect
arrhythmias in drivers during driving. When the ECG is measured through the steering wheel …
arrhythmias in drivers during driving. When the ECG is measured through the steering wheel …
Efficient IoT big data streaming with deep-learning-enabled dynamics
Internet of Medical Things (IoMT) is igniting many emerging smart health applications, by
continuously streaming the big data for data-driven innovations. One critical obstacle in IoMT …
continuously streaming the big data for data-driven innovations. One critical obstacle in IoMT …
Resource and energy efficient implementation of ECG classifier using binarized CNN for edge AI devices
Wearable Artificial Intelligence-of-Things (AIoT) devices demand smart gadgets that are both
resource and energy-efficient. In this paper, we explore efficient implementation of binary …
resource and energy-efficient. In this paper, we explore efficient implementation of binary …