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In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.This study analyses the potential ofby application of theand a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.

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Background: To improve the outcomes of patients with chronic heart failure (CHF), it is important to identify reliable prognostic tools. Early repolarization pattern (ER) on 12‑lead electrocardiogram (ECG) is a predictor of worse outcomes, which has been widely researched in the general population but not in the CHF population.

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The cardiac-related component in chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In a spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal-separation methods are usually based on certain features of signals.

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