AIMC Topic: Electrocardiography

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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 ...

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.

Sensors (Basel, Switzerland)
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutr...

Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

EBioMedicine
BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, pri...

Machine learning-based coronary artery disease diagnosis: A comprehensive review.

Computers in biology and medicine
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is...

A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines.

IEEE transactions on bio-medical engineering
Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we present a novel real-time machine learning system for Arrhythmia classification. The system is based...

Using heart rate profiles during sleep as a biomarker of depression.

BMC psychiatry
BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart ...

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.

JMIR mHealth and uHealth
BACKGROUND: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients wi...

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...

Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network.

Physiological measurement
OBJECTIVE: Ventricular contractions in healthy individuals normally follow the contractions of atria to facilitate more efficient pump action and cardiac output. With a ventricular ectopic beat (VEB), volume within the ventricles are pumped to the bo...