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Continuous monitoring of nocturnal blood pressure is crucial for hypertension management and cardiovascular risk assessment. However, current clinical methods are invasive and discomforting, posing challenges. These traditional techniques often disrupt sleep, impacting patient compliance and measurement accuracy.

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Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users.

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Ballistocardiogram (BCG) captures minute vibrations generated by heart movements. These vibrations are converted into heart rate variability (HRV) indices, allowing their unobtrusive monitoring over extended periods, while reducing the burden on patients or subjects. In this study, to evaluate the agreement between the HRV indices, we compared the HRV indices estimated from the BCG device with those obtained from the gold standard electrocardiogram (ECG).

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Objective: To identify valid parameters of heart rate variability (HRV) and musculoskeletal movements based on ballistocardiography data, which allow predicting the sleep efficiency (SE) index in healthy individuals and patients with insomnia.

Material And Methods: Ten healthy individuals and 14 patients with chronic insomnia were examined using polysomnography and ballistocardiography. A regression analysis of the data was carried out, as well as a reliability check of the resulting mathematical model.

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Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems.

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