Why the study?
Maximizing the acquisition of cardiovascular information through non-invasive methods is crucial to enhance early screening, diagnosis, and evaluation of cardiovascular diseases.
A novel 27-channel cardiovascular signal acquisition system using pressure stimulation was designed and validated with high accuracy using simulators, providing a potential new tool for non-invasive cardiovascular assessment.
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May support multi-channel non-invasive monitoring; leaves open clinical validation in patients.
Cardiovascular diseases (CVDs) pose a significant threat to human health and place considerable strain on healthcare systems. Therefore, it is crucial to maximize the acquisition of cardiovascular information (CVI) through non-invasive methods to enhance early screening, diagnosis, and evaluation of CVDs. Numerous studies have demonstrated that obtaining more CVI by simultaneously acquiring multi-site signals and applying pressure stimulation at specific sites, such as blood pressure measurement, is an effective approach. Based on this evidence, we proposed a novel signal acquisition-and-analysis system to gather comprehensive CVI through a combination of a non-pressure and six pressure-stimulation sub-processes. This system involves the novelty of applying slowly gradual decrease, personalized maximum-pulse amplitude, and blocking blood-flow pressure to six cuffs placed on both arms, wrists, and ankles in a predetermined time sequence. During each sub-process, the system has newly integrated the multi-site simultaneous collection of 27-channel non-invasive signals, including electrocardiogram, heart sound, lung sound, photoplethysmographic-and-pressure pulse. To ensure measurement accuracy, three types of verification-and-calibration instruments were employed. Our results demonstrate that the system can achieve simultaneous acquisition of 27-channel signals during each sub-process, yielding both novel and traditional cardiovascular parameters with high accuracy and good stability. Furthermore, the results suggest that the system can facilitate in-depth research into the relationships between collected signals and CVDs, provide rich raw data for cardiovascular health assessment and disease prediction models based on machine learning algorithms, and offer a new non-invasive method for early diagnosis, evaluation, and prediction of CVDs.
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Liu et al. (2025) studied this question.
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