A microprocessor-based recursive segmentation technique provides an efficient method for analyzing electrophysiological signals such as newborn thoracic impedance waveforms.
May aid neonatal respiratory monitoring via impedance signals; leaves open prospective clinical validation before adoption.
The analysis of electrophysiological signals via a recursive segmentation technique is presented. This method leads to an adaptive time reference linked to the fluctuations of the biorhytms. This numerical process yields a filtered estimation of the signal as well as its first derivative. The simplicity of the equations involved readily leads to the conception of a microprocessor-based structure. This technique is used to analyze thoracic impedance waveforms of the newborn. The cyclic nature of this signal is made use to generate LISSAJOUS patterns which characterize each respiratory cycle. Important features characteristic of each respiratory cycle can be extracted and their evolution with time studied.
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Vasseur et al. (1979) studied this question.