AIMC Topic: Electrocardiography

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Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

IEEE journal of biomedical and health informatics
The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose cha...

Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms.

Computational intelligence and neuroscience
Automated ECG-based arrhythmia detection is critical for early cardiac disease prevention and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia detection with great success. However, the lack of labeled ECG data an...

Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study.

PloS one
To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG), and electroencephalogram (EEG), and to determine whether combination ABP with EEG or CG impro...

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.

Computational intelligence and neuroscience
The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardio...

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review.

Physiological measurement
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiog...

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

Computational intelligence and neuroscience
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect...

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Sensors (Basel, Switzerland)
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely a...

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Sensors (Basel, Switzerland)
The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to es...

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

Computational and mathematical methods in medicine
OBJECTIVE: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diast...

Classification of multi-lead ECG with deep residual convolutional neural networks.

Physiological measurement
. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a novel method to accurately classify multi-lead ECGs using deep residual neural networks.. ECG recordings...