AIMC Topic: Electrocardiography

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ECG beat classification with fractional order differentiator and machine learning techniques.

Biomedical physics & engineering express
Electrocardiogram (ECG) is essential for assessing heart function, but manual analysis is time-consuming and error-prone. Automated ECG analysis can improve early detection of cardiovascular diseases by accurately identifying abnormal beats despite s...

Automated OSAHS detection from ECG using temporal convolutional network.

Scientific reports
Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a prevalent systemic disorder affecting approximately 1 billion people worldwide, associated with severe outcomes such as sudden death and traffic accidents. Despite its significant impact, OSAHS i...

Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection.

Heart (British Cardiac Society)
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often underdiagnosed. Artificial intelligence (AI)-based notification of HCM suspicion on a 12-lead ECG has been proposed to assist patient identification and evaluation. However, there has been no stu...

Detection of Hypokalemia, Hyponatremia, and Hyperkalemia in Heart Failure Patients Using Artificial Intelligence Techniques via Electrocardiography.

Turk Kardiyoloji Dernegi arsivi : Turk Kardiyoloji Derneginin yayin organidir
OBJECTIVE: Detection and monitoring of electrolyte imbalances are essential for the appropriate treatment of many metabolic diseases. However, no reliable and noninvasive tool currently exists for such detection. Electrolyte disorders, particularly i...

An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data.

Scientific reports
Artificial intelligence (AI) algorithms have demonstrated remarkable efficiency in analyzing 12-lead clinical electrocardiogram (ECG) signals. This has sparked interest in leveraging cost-effective and user-friendly smart devices based on single-lead...

Hybrid CNN-BLSTM architecture for classification and detection of arrhythmia in ECG signals.

Scientific reports
This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhy...

Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.

The American journal of cardiology
Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care...

Hybrid deep learning framework for heart disease prediction using ECG signal images.

Scientific reports
With cardiovascular diseases accounting for all other causes of mortality worldwide, an increasing proportion of individuals are being treated for them. To identify the cardiac issue, medical practitioners have to examine electrocardiogram (ECG) data...

FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection.

Scientific reports
Fatigue creates complex challenges that present themselves through cognitive problems alongside physical impacts and emotional consequences. FatigueNet represents a modern multimodal framework that deals with two main weaknesses in present-day fatigu...

Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.

Scientific reports
Monitoring mean arterial pressure (MAP) is essential for ensuring safe general anesthesia. Current practices rely either on non-invasive cuff measurements, which suffer from poor temporal resolution, or invasive arterial lines, which provide excellen...