ANNet: A lightweight neural network for ECG anomaly detection in IoT edge sensors
In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG)
anomaly detection and system level power reduction of wearable Internet of Things (IoT) …
anomaly detection and system level power reduction of wearable Internet of Things (IoT) …
Deep learning applications in ecg analysis and disease detection: An investigation study of recent advances
U Sumalatha, KK Prakasha, S Prabhu… - IEEE Access, 2024 - ieeexplore.ieee.org
Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG)
analysis for early diagnosis and treatment of heart conditions. Recent advancements in …
analysis for early diagnosis and treatment of heart conditions. Recent advancements in …
Interpretable rule mining for real-time ECG anomaly detection in IoT Edge Sensors
Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular
diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal …
diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal …
ECG signal generation based on conditional generative models
Y Xia, W Wang, K Wang - Biomedical Signal Processing and Control, 2023 - Elsevier
Due to the high cost of labeling medical data such as electrocardiogram (ECG) signals, the
performance of classifiers suffers significantly from the lack of annotated data. In recent …
performance of classifiers suffers significantly from the lack of annotated data. In recent …
A Systematic Review on the Use of Consumer-Based ECG Wearables on Cardiac Health Monitoring
This systematic review aims to summarize the consumer wearable devices used for
collecting ECG signals, explore the models or algorithms employed in diagnosing and …
collecting ECG signals, explore the models or algorithms employed in diagnosing and …
Multistage pruning of CNN based ECG classifiers for edge devices
Using smart wearable devices to monitor patients' electrocardiogram (ECG) for real-time
detection of arrhythmias can significantly improve healthcare outcomes. Convolutional …
detection of arrhythmias can significantly improve healthcare outcomes. Convolutional …
Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning
During the lockdown of universities and the COVID-Pandemic most students were restricted
to their homes. Novel and instigating teaching methods were required to improve the …
to their homes. Novel and instigating teaching methods were required to improve the …
Binary ecg classification using explainable boosting machines for iot edge devices
L Xiaolin, W Qingyuan, RC Panicker… - 2022 29th IEEE …, 2022 - ieeexplore.ieee.org
This paper presents an explainable, low-complexity binary electrocardiogram (ECG)
classifier to be deployed in a resource-limited wearable edge device. The presented …
classifier to be deployed in a resource-limited wearable edge device. The presented …
Leveraging statistical shape priors in gan-based ECG synthesis
Electrocardiogram (ECG) data collection during emergency situations is challenging,
making ECG data generation an efficient solution for dealing with highly imbalanced ECG …
making ECG data generation an efficient solution for dealing with highly imbalanced ECG …
Optimized solutions of electrocardiogram lead and segment selection for cardiovascular disease diagnostics
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all
12 leads, which undoubtedly results in a large amount of calculation and is not suitable for …
12 leads, which undoubtedly results in a large amount of calculation and is not suitable for …