Shaikh Anowarul Fattah

Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification

In this paper, two schemes for neuromuscular disease classification from electromyography (EMG) signals are proposed based on discrete wavelet transform (DWT) features. In the first scheme, a few high energy DWT coefficients along with the maximum value are extracted in a frame by frame manner from the given EMG data. Instead of considering only such local information obtained from a single frame, we propose to utilize global statistics which is obtained based on information collected from some consecutive frames. In the second scheme, motor unit action potentials (MUAPs) are first extracted from the EMG data via template matching based decomposition technique. It is well known that not all MUAPs obtained via decomposition are capable of uniquely representing a class. Thus, a novel idea of selecting a dominant MUAP, based on energy criterion, is proposed and instead of all MUAPs, only the dominant MUAP is used for the classification. A feature extraction scheme based on some statistical properties of the DWT coefficients of dominant MUAPs is proposed. For the purpose of classification, the K-nearest neighborhood (KNN) classifier is employed. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms of specificity, sensitivity, and overall classification accuracy.

A wavelet-based dominant feature extraction algorithm for palm-print recognition

In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant wavelet features from each of these local modules. In the selection of the dominant features, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

Identification of noisy AR systems using damped sinusoidal model of autocorrelation function

This letter presents a novel method for minimum-phase autoregressive (AR) system identification at a very low SNR using damped sinusoidal model representation of the autocorrelation function of the noise-free AR signal with guaranteed stability. The new model parameters are estimated solely from the given noisy observations. Then AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters. The simulation results show that the proposed method can estimate the AR system parameters with high accuracy even at an SNR as low as -5dB.

A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is a recently developed technology to detect small intestine diseases, such as bleeding. In this paper, a scheme for automatic bleeding detection from WCE video is proposed based on different statistical measures computed from a new red to green (R/G) pixel ratio intensity plane of RGB color images. Different statistical parameters, namely mean, mode, maximum, minimum, skewness, median, variance, and kurtosis are used to extract variation in spatial characteristics in R/G intensity plane of bleeding and non-bleeding WCE RGB images. Depending on the ability to provide significantly distinguishable characteristics, in the proposed feature vector, median, variance, and kurtosis of R/G ratio values corresponding to a WCE image are considered. For the purpose of classification, K-nearest neighbor (KNN) classifier is employed. From extensive experimentation on several WCE videos collected from a publicly available database, it is observed that the proposed method can successfully detect bleeding and non-bleeding images with high level of accuracy, sensitivity and specificity in comparison to that of some of the existing methods.

Automatic bleeding detection in wireless capsule endoscopy based on RGB pixel intensity ratio

Wireless capsule endoscopy (WCE) is one of the most effective technologies to diagnose gastrointestinal (GI) diseases, such as bleeding in GI tract. Because of long duration of WCE video containing large number images, it is a burden for clinician to detect diseases in real time. In this paper, an automatic bleeding image detection method is proposed utilizing the variation of pixel intensities in RGB color planes. Based on statistical behavior of bleeding and non-bleeding pixel intensities in terms of pixel intensity ratio in different planes, distinguishing color texture feature of an image is developed. Support vector machine (SVM) classifier is employed to detect bleeding and non-bleeding images from WCE videos. From extensive experimentation on real time WCE video recordings, it is found that the proposed method can accurately detect bleeding images with high sensitivity and specificity.

Identification of motor neuron disease using wavelet domain features extracted from EMG signal

Amyotrophic lateral sclerosis (ALS) is a common fatal motor neuron disease that assails the nerve cells in the brain. As the nervous system controls the muscle activity, the electromyography (EMG) signals can be viewed and examined in order to detect the vital features of the ALS disease in individuals. In this paper, the discrete wavelet transform (DWT) based features, which are extracted from a frame of EMG data, are introduced to classify the normal person and the ALS patients. From each frame of EMG data, instead of using a large number of DWT coefficients, the DWT coefficients with higher values as well as their mean and maxima are proposed to be used, which drastically reduces the feature dimension. It is shown that the proposed feature vector offers a high within class compactness and between class separations. For the purpose of classification, the K-nearest neighborhood classifier is employed. In order to demonstrate the classification performance, an EMG database consisted of 5 normal subjects and 5 ALS patients is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.

A face recognition scheme using wavelet-based local features

In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these high-informative horizontal bands precisely, dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.

An Approach to Formant Frequency Estimation at Low Signal-to-Noise Ratio

A new approach for the formant frequency estimation of the voiced speech segments in the presence of noise is presented in this paper. A correlation model for the voiced speech is proposed considering the vocal-tract system as an autoregressive moving average (ARMA) model with a periodic impulse-train excitation. It is shown that the formant frequencies can be directly obtained from the model parameters. An adaptive residue-based least-squares optimization algorithm is proposed to estimate the model parameters, which overcomes the failure of conventional correlation based techniques in estimating formant frequencies at a low signal-to-noise ratio (SNR). The proposed algorithm has been tested on synthetic and natural vowels as well as voiced segments of some naturally spoken sentences from TIMIT database in presence of white Gaussian or babble noises. The experimental results show that the proposed method is more robust to noise than some existing methods even at a low SNR of 0 dB.

An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image

Wireless capsule endoscopy (WCE) is one of the most effective technologies to diagnose gastrointestinal (GI) diseases, such as bleeding in GI tract. Because of long duration of WCE video containing large number images, it is a burden for clinician to detect diseases in real time. In this paper, an automatic bleeding image detection method is proposed utilizing construction of an index image incorporating certain level of information from each plane of RGB color space. Distinguishable color texture feature is developed from index image by histogram. Support vector machine (SVM) classifier is employed to detect bleeding and non-bleeding images from WCE videos. From extensive experimentation on real time WCE video recordings, it is found that the proposed method can accurately detect bleeding images with high sensitivity and specificity.

A Novel Technique for the Identification of ARMA Systems Under Very Low Levels of SNR

In this paper, a novel technique for the identification of minimum-phase autoregressive moving average (ARMA) system from the output observations in the presence of heavy noise is presented. First, starting from the conventional correlation estimator, a simple and accurate ARMA correlation (ARMAC) model in terms of the poles of the ARMA system is presented in a unified manner for white noise and impulse-train excitations. The AR parameters of the ARMA system are then obtained from the noisy observations by developing and using a residue-based least-squares correlation-fitting optimization technique that employs the proposed ARMAC model. As for the estimation of the MA parameters, it is preceded by the application of a new technique intended to reduce the noise present in the residual signal that is obtained by filtering the noisy ARMA signal via the estimated AR parameters. A scheme is then devised whereby the task of MA parameter estimation is transformed into a problem of correlation-fitting of the inverse autocorrelation function corresponding to the noise-compensated residual signal. In order to demonstrate the effectiveness of the proposed method, extensive simulations are performed by considering synthetic ARMA systems of different orders in the presence of additive white noise and the results are compared with those of some of the existing methods. It is shown that the proposed method is capable of estimating the ARMA parameters accurately and consistently with guaranteed stability for signal-to-noise ratio (SNR) levels as low as -5 dB. Simulation results are also provided for the identification of a human vocal-tract system using natural speech signals showing a superior performance of the proposed technique in terms of the power spectral density of the synthesized speech signal.