Key result
A novel method using Laguerre Model Blind System Identification on two PPG sensors successfully estimated a cardiac output waveform showing good agreement with typical human morphology.
A novel signal-processing algorithm using two wearable PPG sensors can estimate the human cardiac output waveform with good morphological agreement.
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May enable wearable cardiac output estimation; hypothesis-generating and requires prospective validation before clinical use.
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 the cardiac output waveform. Initial results implementing the method on data derived from a human subject is presented. Results show good agreement between the morphology of the estimated waveform and the typical morphology of the human cardiac output waveform.
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McCombie et al. (2005) studied this question. Laguerre Model Blind System Identification and deconvolution method using two PPG sensors was evaluated on Morphology of the estimated cardiac output waveform. A novel method using Laguerre Model Blind System Identification on two PPG sensors successfully estimated a cardiac output waveform showing good agreement with typical human morphology.
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