An improvement of Brown's classification is described... The new classification reflects the amplitude and time-relation changes of the Bcg. A method for estimating the mean values of Bcg elements, based on the principle of calculation of all corresponding values during the complete 2-3 respiratory cycles is described
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Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.
Nat Sci Sleep
July 2024
Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, V3T 0A3, Canada.
Purpose: Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved.
View Article and Find Full Text PDFBCG Signal Quality Assessment Based on Time-Series Imaging Methods.
Sensors (Basel)
November 2023
Department of Mechatronics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts.
View Article and Find Full Text PDFNon-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review.
Front Bioeng Biotechnol
October 2023
Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates.
The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance.
View Article and Find Full Text PDFClassification of health deterioration by geometric invariants.
Comput Methods Programs Biomed
September 2023
Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic.
Background And Objectives: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress.
View Article and Find Full Text PDFClassification of Sleep-Wake State in Ballistocardiogram system based on Deep Learning.
Annu Int Conf IEEE Eng Med Biol Soc
July 2022
Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders. Polysomnography (PSG), the gold standard, is intrusive and inconvenient for regular/long-term sleep monitoring. Many sleep-monitoring techniques have recently seen a resurgence as a result of the rise of neural networks and advanced computing.
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