Hamid Hassanpour

Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques

The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

Using Hidden Markov Models for paper currency recognition

Accurate characterization is an important issue in paper currency recognition system. This paper proposes a robust paper currency recognition method based on Hidden Markov Model (HMM). By employing HMM, the texture characteristics of paper currencies are modeled as a random process. The proposed algorithm can be used for distinguishing paper currency from different countries. A similarity measure has been used for the classification in the proposed algorithm. To evaluate the performance of the proposed algorithm, experiments have been conducted on more than 100 denominations from different countries. The results indicate 98% accuracy for recognition of paper currency.

A time--frequency approach for noise reduction

This paper proposes a technique for reducing noise from a signals time series using a time-frequency distribution. The technique is based on the SVD of the matrix associated with the time-frequency representation of the signal. In this approach the time-frequency representation of the signal is initially divided into signal subspace and noise subspace using singular values of the time-frequency matrix as a criterion for space division. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix enhances the information embedded in the time-frequency representation of the signal. The proposed approach utilizes the Savitzky-Golay low-pass filter for noise attenuation from the singular vectors. The results of applying the proposed method on both synthetic signals and newborn EEGs indicate superiority of the proposed technique over the existing one in reducing noise from signals.

Time-frequency based newborn EEG seizure detection using low and high frequency signatures

The nonstationary and multicomponent nature of newborn EEG seizures tend to increase the complexity of the seizure detection problem. In dealing with this type of problem, time-frequency based techniques were shown to outperform classical techniques. Neonatal EEG seizures have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. Seizure detection techniques have been proposed that concentrate on either low frequency or high frequency signatures of seizures. They, however, tend to miss seizures that reveal themselves only in one of the frequency areas. To overcome this problem, we propose a detection method that uses time-frequency seizure features extracted from both low and high frequency areas. Results of applying the proposed method on five newborn EEGs are very encouraging.

Feature extraction for paper currency recognition

This paper proposes a new technique for paper currency recognition. In this technique, three characteristics of paper currencies including size, color and texture are used in the recognition. By using image histogram, plenitude of different colors in a paper currency is computed and compared with the one in the reference paper currency. The Markov chain concept has been employed to model texture of the paper currencies as a random process. The method proposed in this paper can be used for recognizing paper currencies from different countries. In this method, using only one intact example of paper currency from each denomination is enough for training the system. We tested this method on more than 100 denominations from different countries, and the system was able to recognize 95% of data, correctly.

Video Frame’s Background Modeling: Reviewing the Techniques

Background modeling is a technique for extracting moving objects in video frames. This technique can be used in ma-chine vision applications, such as video frame compression and monitoring. To model the background in video frames, initially, a model of scene background is constructed, then the current frame is subtracted from the background. Even-tually, the difference determines the moving objects. This paper evaluates a number of existing background modeling techniques in term of accuracy, speed and memory requirement.

EEG spike detection using time-frequency signal analysis

The paper presents a new method for detecting EEG spikes. The method is based on the time-frequency distribution of the signal. As spikes are short time broadband events, they are represented as ridges in the time-frequency domain. In this domain, the high instantaneous energy of spikes allows them to be distinguishable from the background. 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.

Time domain signal enhancement based on an optimized singular vector denoising algorithm

This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods.

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model.

Neonatal EEG seizure detection using spike signatures in the time-frequency domain

This paper presents an improved time-frequency (TF) based technique for newborn EEG seizure detection. The original technique analyses successive spikes intervals of the EEG signal in the TF domain to discriminate between seizure and nonseizure activities. In this paper improvement on the original approach is achieved by using a new spike detection technique. In this technique the TF of the signal is enhanced before the actual spike detection scheme is applied. Then, two frequency slices are extracted from the higher frequency area of the TF distribution to detect the spikes. The extracted frequency slices are subjected to the smoothed nonlinear energy operator to accentuate the spike signatures. Histogram of successive spikes intervals is then used as a feature for seizure detection. In the presented technique the EEG data are segmented into 4-second epochs. A k-nearest neighbour algorithm is employed to classify the EEG epochs into seizure and nonseizure groups. The performance of the presented technique is evaluated using the EEG data of five neonates.