AIMC Topic: Electrocardiography

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Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review.

Journal of the American Heart Association Apr 10, 2025
Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative revi...
Amyloidosis Cardiomyopathies Echocardiography Electrocardiography Predictive Value of Tests Humans Artificial Intelligence
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Optimized driver fatigue detection method using multimodal neural networks.

Scientific reports Apr 10, 2025
Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study introduces a comprehensive approach using multimodal neural networks, leveraging the DROZY dataset, wh...
Fatigue Humans Neural Networks, Computer Algorithms Accidents, Traffic Automobile Driving Electrocardiography Electroencephalography
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

BMC cardiovascular disorders Apr 7, 2025
A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular monitoring is essential for effective management, as early detection and timely treatment greatly imp...
Predictive Value of Tests Signal Processing, Computer-Assisted Arrhythmias, Cardiac Diagnosis, Computer-Assisted Electrocardiography Action Potentials Heart Rate Humans Machine Learning Algorithms Reproducibility of Results
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ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Sensors (Basel, Switzerland) Apr 5, 2025
Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedde...
Signal Processing, Computer-Assisted Computer Security Autoencoder Humans Electrocardiography
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An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Scientific reports Apr 3, 2025
Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative...
Mental Disorders Female Adult Male Humans Neural Networks, Computer Nervous System Diseases Deep Learning Electrocardiography Electroencephalography
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Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Scientific reports Apr 3, 2025
Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring...
Electroencephalography Arrhythmias, Cardiac Heart Arrest ROC Curve Prognosis Electrocardiography Humans Area Under Curve Machine Learning Female Adult Coma Aged Male Middle Aged
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An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

IEEE transactions on biomedical circuits and systems Apr 3, 2025
Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most of them have relatively low energy efficiency, and lack configurability in classification leads number and inference algorithm models. A multi-lead EC...
Algorithms Wearable Electronic Devices Electrocardiography Signal Processing, Computer-Assisted Humans Neural Networks, Computer
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Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Sensors (Basel, Switzerland) Apr 2, 2025
Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and tr...
Humans Machine Learning Female Electrocardiography Adult Support Vector Machine Male Long QT Syndrome Genotype
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AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

Journal of Korean medical science Mar 31, 2025
This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively...
Acute Coronary Syndrome Adult Pilot Projects Emergency Service, Hospital Clinical Decision-Making Male Humans Coronary Angiography Electrocardiography Middle Aged Female Chest Pain Artificial Intelligence Decision Making Aged Retrospective Studies
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Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

Open heart Mar 31, 2025
BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the risk of stroke and heart failure. Early detection is crucial, yet traditional methods struggle with AF's transient nature. This study investigates how au...
Heart Rate Electrocardiography Humans Predictive Value of Tests Databases, Factual Atrial Fibrillation Male Female Middle Aged Reproducibility of Results Deep Learning Aged
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