AIMC Topic: Electrocardiography

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ECG data compression using a neural network model based on multi-objective optimization.

PloS one
Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms o...

Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model.

Biosignals learning and synthesis using deep neural networks.

Biomedical engineering online
BACKGROUND: Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the or...

A deep convolutional neural network model to classify heartbeats.

Computers in biology and medicine
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrh...

A Wireless ExG Interface for Patch-Type ECG Holter and EMG-Controlled Robot Hand.

Sensors (Basel, Switzerland)
This paper presents a wearable electrophysiological interface with enhanced immunity to motion artifacts. Anti-artifact schemes, including a patch-type modular structure and real-time automatic level adjustment, are proposed and verified in two wirel...

Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification.

Scientific reports
Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of ...

Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine.

Computers in biology and medicine
Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to c...

Premature ventricular contraction detection combining deep neural networks and rules inference.

Artificial intelligence in medicine
Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpa...