IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jan 2008
A novel noninvasive measurement technique for analyzing the pressure pulse waveform of the radial artery.
Previous noninvasive measurements of the pulse waveform of the radial artery have not employed standard positioning procedures. Here, we propose a new noninvasive measuring apparatus that has a two-axis mechanism and employs a standard positioning procedure for detecting the optimal site for accurately measuring the pressure pulse waveform (PPW). A modified sensor was designed to simultaneously measure the arterial diameter changed waveform (ADCW) and PPW. ⋯ The PPW analysis used the harmonic components in the frequency domain. We found that the fourth harmonic of the Fourier series differed significantly between the groups (p = 0.0039), which is consistent with previous studies. The results indicate that our noninvasive measurement apparatus is very suitable for analyzing the PPW of the radial artery.
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IEEE Trans Biomed Eng · Jan 2008
Automatic identification of return of spontaneous circulation during cardiopulmonary resuscitation.
The main problem during pulse check in out-of-hospital cardiac arrest is the discrimination between normal pulse-generating rhythm (PR) and pulseless electrical activity (PEA). It has been suggested that circulatory information can be acquired by measuring the thoracic impedance via the defibrillator pads. ⋯ Using realistic data analyzed over a duration of 3 s, our system correctly identifies 90.0% of the segments with rhythm being pulseless electrical activity, and 91.5% of the normal pulse rhythm segments. Automatic identification of pulse could avoid unnecessary pulse checks and thereby reduce no-flow time and potentially increase the chance of survival.
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IEEE Trans Biomed Eng · Jan 2008
Atlas-based indexing of brain sections via 2-D to 3-D image registration.
A 2-D to 3-D nonlinear intensity-based registration method is proposed in which the alignment of histological brain sections with a volumetric brain atlas is performed. First, sparsely cut brain sections were linearly matched with an oblique slice automatically extracted from the atlas. ⋯ We demonstrate the method and evaluate its performance with simulated and real data experiments. An atlas-guided segmentation of mouse brains' hippocampal complex, retrieved from the Mouse Brain Library (MBL) database, is demonstrated with the proposed algorithm.
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IEEE Trans Biomed Eng · Jan 2008
Optimized wavelets for blind separation of nonstationary surface myoelectric signals.
Surface electromyography (EMG) signals detected over the skin surface may be mixtures of signals generated by many active muscles due to poor spatial selectivity of the recording. In this paper, we propose a new method for blind source separation (BSS) of nonstationary signals modeled as linear instantaneous mixtures. The method is based on whitening of the observations and rotation of the whitened observations. ⋯ On a set of simulated signals, for 10-dB signal-to-noise ratio (SNR), the cross-correlation coefficient between original and estimated sources was 0.92 +/- 0.07 with wavelet optimization, 0.74 +/- 0.09 with the wavelet leading to the poorest performance, 0.85 +/- 0.07 with Wigner-Ville distribution, 0.86 +/- 0.07 with Choi-Williams distribution, and 0.73 +/- 0.05 with second-order statistics. In experimental conditions, when the flexor carpi radialis and pronator teres were concomitantly active for 50% of the time, crosstalk was 55.2 +/- 10.0% before BSS and was reduced to 15.2 +/- 6.3% with wavelet optimization, 30.1 +/- 15.0% with the worst wavelet, 28.3 +/- 12.3% with Wigner-Ville distribution, 26.2 +/- 12.0% with Choi-Williams distribution, and 35.1 +/- 15.5% with second-order statistics. In conclusion, the proposed approach resulted in better performance than previous methods for the separation of nonstationary myoelectric signals.
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IEEE Trans Biomed Eng · Jan 2008
Statistical modeling of cardiovascular signals and parameter estimation based on the extended Kalman filter.
Cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP) contain useful information such as heart rate, respiratory rate, and pulse pressure variation (PPV). We present a novel state-space model of cardiovascular signals and describe how it can be used with the extended Kalman filter (EKF) to simultaneously estimate and track many cardiovascular parameters of interest using a unified statistical approach. ⋯ Our results demonstrate the ability of the algorithm to estimate and track several clinically relevant features of cardiovascular signals. We illustrate how the algorithm can be used to elegantly solve several actively researched and clinically significant problems including heart and respiratory rate estimation, artifact removal, pulse morphology characterization, and PPV estimation.