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Arrhythmias, Cardiac

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A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors.

IEEE transactions on biomedical circuits and systems
Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However, the implementation of ultralow power ANN-CAC is challenging due to the intensive computations. Mor...

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

Journal of electrocardiology
BACKGROUND: The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be classified into many types, including life-threatening and non-life-thr...

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE.

Australasian physical & engineering sciences in medicine
Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of ele...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Computational and mathematical methods in medicine
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.

Journal of healthcare engineering
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a chal...

Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of t...

Cardiac arrhythmia detection using deep learning: A review.

Journal of electrocardiology
Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operat...

Energy-Efficient Intelligent ECG Monitoring for Wearable Devices.

IEEE transactions on biomedical circuits and systems
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart prob...

A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet.

Sensors (Basel, Switzerland)
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning ...

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

Sensors (Basel, Switzerland)
The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutio...