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This paper presents an adaptive multi-channel (AMC) approach for the reconstruction of the central aortic blood pressure (BP) waveform from multiple peripheral BP measurements. In contrast to most of the previously developed single-channel methods for estimating central aortic BP waveform, the key merit of the AMC algorithm is its ability to be individualized without any prior model training or parameter...
A method for estimating pulse wave velocity (PWV) using circulatory waveform signals derived from multiple photoplethysmograph (PPG) sensors is described. The method employs two wearable in-line PPG sensors placed at a known distance from one another at the ulnar and digital artery. A technique for calibrating the measured pulse wave velocity to arterial blood pressure using hydrostatic pressure variation...
A method for estimating cardiovascular dynamics and cardiac output waveforms using signals derived from two PPG sensors is presented. The method employs a novel signal-processing algorithm known as Laguerre model blind system identification to identify the vascular dynamics associated with the measured PPG signals. A unique deconvolution method is then used with the identified Laguerre models to estimate...
We have developed a new technique, which may enable a more accurate, complete characterization of the circulatory system, including local or global hydrodynamic phenomena, using multiple measurements from several anatomic locations and/or multiple modalities. This technique, Laguerre model blind system identification (LMBSI), uses a Laguerre function series expansion to provide a compact but complete...
A new tool for characterizing systemic and local hemodynamic behavior in real-time has been developed. Through the evaluation of two peripheral circulatory waveform measurements (e.g. continuous measurements such as pressure or flow or volumetric change) this new signal-processing algorithm generates two compact, low order models that describe the distinct branch dependent features of the measured...
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