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We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine ( = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other ( < 0.05). We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.
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http://dx.doi.org/10.1089/tmj.2020.0507 | DOI Listing |
Rev Cardiovasc Med
August 2025
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, LE3 9QP Leicester, UK.
Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum.
View Article and Find Full Text PDFPsychophysiology
September 2025
Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany.
Exercise influences visual processing and is accompanied by neural and physiological changes in the body. Yet, the underlying mechanisms by which neural and physiological responses to exercise impact ensuing perception remain poorly understood. In particular, the effects of exercise-induced cardiac changes on visual perception and electrophysiological activity are unclear.
View Article and Find Full Text PDFIEEE Trans Cybern
September 2025
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective.
View Article and Find Full Text PDFCell Biol Int
September 2025
Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, India.
Diabetic cardiomyopathy (DCM) is a progressive heart disorder associated with diabetes mellitus, leading to structural and functional cardiac abnormalities. The mechanisms responsible include renin-angiotensin-aldosterone (RAAS) activation, inflammation, apoptosis, and metabolic disturbances. Despite well-established epidemiological links, treatments for DCM are elusive.
View Article and Find Full Text PDFFront Artif Intell
August 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India.
Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. In the case of ECG as a one-dimensional signal, 1-D CNNs are deployed, whereas in the case of a 2D-represented ECG signal, i.e.
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