1,721,024 research outputs found
A time-frequency approach for newborn seizure detection
No full textTechniques previously designed for seizure detection in newborns using the electroencephalogram (EEG) have been relatively inefficient due to their assumption of local stationarity of the EEG. To overcome the problem raised by the nonstationarity of the EEG signal, current methods are extended to a time-frequency approach. This allows the analysis and characterization of the different newborn EEG patterns that are intended to be the first step toward an automatic time-frequency seizure detection and classification. An in-depth analysis of both the autocorrelation and spectrum seizure detection techniques identified the detection criteria that can be extended to the time-frequency domain. The selected method uses a high-resolution reduced interference time-frequency distribution referred to as the B-distribution (BD). Here, the authors present the various patterns of observed time-frequency seizure signals and relate them to current knowledge of seizures. In particular, initial results indicate that a quasilinear instantaneous frequency (IF) can be used as a critical feature of the EEG seizure characteristic
Reduced bias time-frequency peak filtering
No full textTime-frequency peak filtering (TFPF) allows the reconstruction of signals from observations corrupted by additive noise by encoding the noisy signal as the instantaneous frequency (IF) of an FM analytic signal. IF estimation is then performed on the analytic signal using the peak of a time-frequency distribution (TFD) to recover the filtered signal. This method is biased when the peak of the Wigner-Ville distribution (WVD) is used to estimate the encoded signal's instantaneous frequency. We derive the windowed WVD window length that achieves a reduced bias when TFPF is used for the class of deterministic bandlimited non-stationary multicomponent signals in additive white Gaussian nois
A Discrete Time and Frequency Wigner Distribution: Properities and Implementation
No full textTime-frequency distributions are used in the analysis and processing of nonstationary signals. The Wigner-Ville distribution (WVD) is a fundamental time-frequency distribution uniquely satisfying many desirable mathematical properties. The realisation of this distribution for hardware or software platforms requires a discrete version. Historically the majority of the work on deriving discrete versions of the WVD has focused on creating alias-free distributions, often resulting in a loss of some desirable properties. Here a new discrete time and frequency WVD will be presented for nonperiodic signals and will be examined both in terms of its properties and aliasing. In particular unitarity, an assumed property for optimum time-frequency detection and signal estimation, and invertibility, a useful property especially for time-frequency filtering, will be examined. An efficient implementation of the distribution using standard real-valued fast Fourier transforms will also be presented
A fast incremental approach for accurate measurement of the displacement field
No full textA fast method for accurate measurement of displacement field that combines template matching and differential techniques is proposed. This method, which is able to deal with the problems of non-rigid object movement as well as large inter frame displacements, operates in three steps. First, a sparse displacement field is obtained by a classic template matching technique. Then, this information is propagated through the overall image to obtain a dense displacement field. Finally, this field is considered as approximate solution before the use of classical differential technique for optical flow estimation. To illustrate the efficiency of the proposed procedure, a number of experimental results using both synthetic and real images are presented and discussed. The results show significant improvement over those obtained using standard multigrid approaches, especially in the cases of textured, very structured images or large inter frame displacements
Time-frequency analysis of high-frequency activity for seizure detection and tracking in neonate
No full textPublication in the conference proceedings of EUSIPCO, Toulouse, France, 200
Signal enhancement by time-frequency peak filtering
No full textTime-frequency peak filtering (TFPF) allows the reconstruction of signals from observations corrupted by additive noise by encoding the noisy signal as the instantaneous frequency (IF) of a frequency modulated (FM) analytic signal. IF estimation is then performed on the analytic signal using the peak of a time-frequency distribution (TFD) to recover the filtered signal. This method is biased when the peak of the Wigner-Ville distribution (WVD) is used to estimate the encoded signal's instantaneous frequency. We characterize a class of signals for which the method implemented using the pseudo WVD is approximately unbiased. This class contains deterministic bandlimited nonstationary multicomponent signals in additive white Gaussian noise (WGN). We then derive the pseudo WVD window length that gives a reduced bias when TFPF is used for signals from this class. Testing of the method on both synthetic and real life newborn electroencephalogram (EEG) signals shows clean recovery of the signals in noise level down to a signal-to-noise ratio (SNR) of -9 dB
Minimum Classification Error Using Time-Frequency Analysis
No full textFor certain classes of signals, such as time varying signals, classical classification algorithms are not suitable. Hence, time-frequency hased techniques are employed for classification of these types of signals. In this paper we propose data-driven time frequency representations kernel optimization, that leads to the minimum classification error (MCE) for nonstationary signal classification. Our central issue is to determine the optimal kernel parameters and best distance measure to achieve the MCE performance measure. The minimum classification error achievable using optimized kernels is investigated for two types of nonstationary signals: namely simulated chirp signals and real-life newborn EEG signals. For the EEG signals a classification error as low as 4.6% was achieved
Newborn EEG Seizure Detection Using Signal Structural Complexity
Get PDFPublication in the conference proceedings of EUSIPCO, Viena, Austria, 200
A time-frequency approach for spike detection
No full textThis paper presents a new time frequency based spike detection technique. As spikes are broadband events with high instantaneous energy, they tend to have a larger energy than the background at high frequencies. To detect spikes, the time frequency distribution of the signal of interest is first enhanced to attenuate the noise. Two frequency slices of the enhanced time frequency distribution are then extracted and subjected to the smoothed nonlinear energy operator (SNEO). Finally, the output of the SNEO is thresholded to localise the position of the spikes in the signal. The SNEO is employed to accentuate the spike signature in the extracted frequency slices. A spike is considered to exist in the time domain signal if a signature of the spike is detected at the same position in both frequency slices. The performance of the proposed method is evaluated using both synthetic and newborn electroencephalogram (EEG) signals
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