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Implications of an Individualized Resuscitation Strategy Using Continuous Rhythm and Physiologic Status Assessment During Ongoing CPR.
Resuscitation
January 2025
Department of Medicine, University of Washington, Seattle, WA; King County Emergency Medical Services, Seattle-King County Department of Public Health, Seattle, WA.
Background: Prior studies have proposed defibrillator biosignal algorithms which characterize cardiac arrest rhythm and physiologic status. We evaluated whether a novel, individualized resuscitation strategy that integrates multiple ECG and impedance-based algorithms could reduce CPR interruptions and better align rescuer actions with patient-specific physiology.
Methods: In a retrospective cohort of ventricular fibrillation out-of-hospital cardiac arrests, observed rescuer actions (rhythm analysis, shock delivery, pulse checks, and drug therapy) were compared to hypothetical actions recommended by the proposed individualized strategy.
Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning.
J Cardiovasc Magn Reson
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
Purpose: To investigate image quality and agreement of derived cardiac function parameters in a novel joint image reconstruction and segmentation approach based on disentangled representation learning, enabling real-time cardiac cine imaging during free-breathing.
Methods: A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with MR scans specifically acquired for model development. An exploratory feasibility study evaluated the method on undersampled real-time acquisitions using an in-house developed spiral bSSFP pulse sequence in eight healthy participants and five patients with intermittent atrial fibrillation.
Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study.
JMIR Cardio
January 2025
School of Life Science and Technology, University of Electronic Science and Technology of China, Research Building C348A, 3rd Fl, Chengdu, 611731, China, 86 18030493605.
Background: Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG) , which can be observed via wearable sensors.
View Article and Find Full Text PDFEnhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.
Comput Biol Med
January 2025
Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India. Electronic address:
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of deaths caused by CVD. Artificial intelligence (AI) is used to categorize and accurately predict various CVDs.
View Article and Find Full Text PDFA Nomogram utilizing ECG P-wave parameters to predict recurrence risk following catheter ablation in paroxysmal atrial fibrillation.
J Cardiothorac Surg
January 2025
Department of Cardiology, Fujian Medical University Union Hospital, Fujian Heart Medical Center, Fujian Institute of Coronary Heart Disease, Fujian Clinical Medical Research Center for Heart and Macrovascular Disease, Fuzhou, 350001, China.
Objective: The objective of this study is to assess the predictive utility of perioperative P-wave parameters in patients with paroxysmal atrial fibrillation (PAF) undergoing catheter ablation, and to develop a predictive model using these parameters.
Methods: A total of 213 patients with PAF undergoing catheter ablation were retrospectively analyzed. P-wave parameters were measured within 3 days preoperatively and on the day postoperatively to determine their predictive significance for postoperative PAF recurrence.
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