IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Apr 2003
Clinical Trial Controlled Clinical TrialReduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic.
In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure (ABP) measurement. The purpose of this paper is to improve the accuracy in the arterial pressure measurement by reducing interference in the OAs using a recursive weighted regression algorithm (RWRA). This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. ⋯ It was found that the average difference between the pooled blood pressures measured by the auscultation and those by the oscillometry combined with the RWRA was found to be only 4.9 mmHg. Clinical results demonstrated that the proposed RWRA is more robust than the traditional curve fitting algorithm (TCFA). We conclude that the proposed RWRA can be applied to effectively improve the accuracy of the oscillometric blood pressure measurement.
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IEEE Trans Biomed Eng · Apr 2003
Comparative StudyMethod for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio.
Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. ⋯ It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.