AIMC Topic: Electrocardiography

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Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring.

Mathematical biosciences and engineering : MBE
About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximatel...

Non-contact wearable synchronous measurement method of electrocardiogram and seismocardiogram signals.

The Review of scientific instruments
Cardiovascular disease is one of the leading threats to human lives and its fatality rate still rises gradually year by year. Driven by the development of advanced information technologies, such as big data, cloud computing, and artificial intelligen...

[Artificial intelligence applied to the electrocardiogram, or is there really a needle in a haystack?].

Recenti progressi in medicina
Artificial intelligence applied to the standard Ecg (Ai-Ecg) is able to enormously enhance the performance of a diagnostic test that has been in use for over 100 years. The tool can in fact identify diseases, cardiac and non-cardiac, hidden or forese...

[Why artificial intelligence applied to the electrocardiogram is not yet clinical routine?].

Recenti progressi in medicina
Artificial intelligence opens up multiple application scenarios to the electrocardiogram (Ai-Ecg) in various clinical settings, with potentially extremely interesting expected results. However, the introduction of prediction models of clinical diagno...

[Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast i...

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.

European heart journal
AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG cri...

Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on local...

[Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.