AIMC Topic: Electrocardiography

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Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram.

Sensors (Basel, Switzerland)
Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convoluti...

Affect and stress detection based on feature fusion of LSTM and 1DCNN.

Computer methods in biomechanics and biomedical engineering
The impact of emotions on health, especially stress, is receiving increasing attention. It is important to provide a non-invasive affect detection system that can be continuously monitored for a long period of time. Multi-sensor fusion strategies can...

Correlation analysis of deep learning methods in S-ICD screening.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of EC...

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning.

Journal of electrocardiology
BACKGROUND: Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to ...

ECG signal feature extraction trends in methods and applications.

Biomedical engineering online
Signal analysis is a domain which is an amalgamation of different processes coming together to form robust pipelines for the automation of data analysis. When applied to the medical world, physiological signals are used. It is becoming increasingly c...

Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

Medical engineering & physics
BACKGROUND: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios.

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

Scientific reports
Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep c...

Artificial intelligence and body composition.

Diabetes & metabolic syndrome
AIMS: Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease i...

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for pati...