AIMC Topic: Electrocardiography

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Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data.

Sensors (Basel, Switzerland)
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocar...

SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection.

Physiological measurement
Accurate detection of electrocardiogram (ECG) waveforms is crucial for computer-aided diagnosis of cardiac abnormalities. This study introduces SEResUTer, an enhanced deep learning model designed for ECG delineation and atrial fibrillation (AF) detec...

Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach.

Sensors (Basel, Switzerland)
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with m...

Personalized ECG monitoring and adaptive machine learning.

Journal of electrocardiology
This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, inc...

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach.

The Canadian journal of cardiology
BACKGROUND: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artifici...

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires ...

Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study.

BMC medical education
BACKGROUND: The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting e...

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to progno...

Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration.

IISE transactions on occupational ergonomics and human factors
OCCUPATIONAL APPLICATIONSAn understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (H...