IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · May 2008
A method for the analysis of respiratory sinus arrhythmia using continuous wavelet transforms.
A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. ⋯ On the one hand, wavelet analysis presents a sufficient frequency-resolution to handle low respiratory frequencies, for which time frames should be long in Fourier-based analysis. On the other hand, it is able to track fast variations of the signals in both amplitude and phase for which time frames should be short in Fourier-based analysis.
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IEEE Trans Biomed Eng · May 2008
Estimation of the aortic pressure waveform and beat-to-beat relative cardiac output changes from multiple peripheral artery pressure waveforms.
We introduce a patient- and time-specific technique to estimate the clinically more relevant aortic pressure (AP) waveform and beat-to-beat relative changes in cardiac output (CO) from multiple peripheral artery pressure (PAP) waveforms distorted by wave reflections. The basic idea of the technique is to first estimate the AP waveform by applying a new multichannel blind system identification method that we have developed (rather than the conventional generalized transfer function) to the PAP waveforms and then estimate the beat-to-beat proportional CO by fitting a Windkessel model to the estimated waveform in which wave distortion should be attenuated. ⋯ These estimation errors represent substantial improvements compared to those obtained with several alternative PAP waveform analysis techniques. With further successful testing, the new technique may ultimately be employed for automated and less invasive monitoring of central hemodynamics in various cardiovascular patients.
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IEEE Trans Biomed Eng · May 2008
Analysis of FMRI data with drift: modified general linear model and Bayesian estimator.
The slowly varying drift poses a major problem in the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, based on the observation that noise in fMRI is long memory fractional noise and the slowly varying drift resides in a subspace spanned only by large scale wavelets, we examine a modified general linear model (GLM) in wavelet domain under Bayesian framework. ⋯ Results obtained from simulated as well as real fMRI data show that the proposed Bayesian estimator can accurately capture the noise structure, and hence, result in robust estimation of the parameters in GLM. Besides, the proposed model selection criterion works well and could efficiently remove the drift.
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IEEE Trans Biomed Eng · Apr 2008
Analysis of cultured neuronal networks using intraburst firing characteristics.
It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. ⋯ The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.
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IEEE Trans Biomed Eng · Apr 2008
Robust unsupervised detection of action potentials with probabilistic models.
We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesian probability theory, the detection of action potentials is posed as a model selection problem. Our technique provides a robust performance over a wide range of simulated conditions, and compares favorably to selected supervised and unsupervised detection techniques.