IEEE transactions on bio-medical engineering
-
IEEE Trans Biomed Eng · Jun 2005
Comparative StudyA 2-D ECG compression method based on wavelet transform and modified SPIHT.
A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. ⋯ Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.
-
IEEE Trans Biomed Eng · Jun 2005
Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.
This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. ⋯ The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.
-
IEEE Trans Biomed Eng · May 2005
An input-delay neural-network-based approach for piecewise ECG signal compression.
We propose an input delay neural network (IDNN) based time series prediction algorithm for compressing electrocardiogram (ECG) signals. Our algorithm has been tested and successfully compared vis-à-vis other popular techniques for its compression efficiency and reconstruction capability.
-
IEEE Trans Biomed Eng · May 2005
A 1.48-mW low-phase-noise analog frequency modulator for wireless biotelemetry.
This paper presents a low-phase-noise, hybrid LC-tank, analog frequency modulator for wireless biotelemetry employing on-chip NMOS varactors in the inversion region as the frequency tuning element. We demonstrate that a correct estimate for the destination signal-to-noise ratio, which quantifies the quality of the wirelessly received signal in a frequency-modulated biotelemetry system, is only achieved after taking into account the large-signal oscillation effect on the tank varactor. ⋯ The VCO is successfully interfaced with a penetrating silicon microelectrode with 700 microm2 iridium recording sites for wireless in vitro recording of a 50 Hz simulated normal sinus rhythm signal from saline over a distance of approximately 0.25 m. Given a typical gain of approximately 40 dB for fully integrated front-end bioamplifiers, a wireless recording microsystem employing this VCO would be capable of detecting input biopotentials down to the submilivolt range.
-
IEEE Trans Biomed Eng · May 2005
Estimation of the cortical connectivity by high-resolution EEG and structural equation modeling: simulations and application to finger tapping data.
Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. ⋯ Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.