Rohilah Sahak

Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network

Asphyxia occurs in infants with neurological level disturbance, which is found to affect sound of cry produced by infants. The infant cry signals with asphyxia have distinct patterns which can be recognized with pattern classifiers such as Artificial Neural Network (ANN). This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from their cries, of ages from zero to seven months old, with an input feature reduction algorithm, Orthogonal Lest Square (OLS) analysis, in contrast to direct selection. The infant cry waveform served as input to Mel Frequency Cepstrum (MFC) analysis for feature extraction. The MLP classifier performance was examined with different combination in number of coefficients, filter bank and hidden nodes. It is found that the OLS algorithm is effective in enhancing the accuracy of MLP classifier while reducing the computation load. Both the average and highest MLP classification accuracies with coefficients being ranked by OLS algorithm have consistently displayed better score than those by direct selection. The highest MLP classification accuracy of 94% is obtained with 40 filter banks, 12 highly ranked MFC coefficients and 15 hidden nodes.

Performance of Combined Support Vector Machine and Principal Component Analysis in recognizing infant cry with asphyxia

Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel was examined. Experimental results showed that SVM with RBF kernel yields good performance. The classification accuracy in classifying infant cry with asphyxia using the SVM-PCA is 95.86%.

Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia

The infant cry signals with asphyxia have distinct patterns which can be recognized using pattern classifiers such as Artificial Neural Network (ANN). This study investigates the effect of selecting infant cry features using the Binary Particle Swarm Optimization on the performance of Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from cry signals. The feature extraction process was performed by MFCC analysis. The MLP classifier performance was examined using various combination of number of coefficients. It was found that the BPSO helps to enhance the classification accuracy of MLP classifier while reducing the computational load. The highest MLP classification accuracy achieved was 95.07%, which was obtained when 26 MFCC filter banks, 14 selected MFC coefficients and 5 hidden nodes were used.

Classification of infant cries with hypothyroidism using Multilayer Perceptron neural network

Hypothyroidism occurs in infants with insufficient production of hormones by the thyroid gland. The cry signals of babies with hypothyroidism have distinct patterns which can be recognized with pattern classifiers such as Multilayer Perceptron (MLP) artificial neural network. This study investigates the performance of the MLP in discriminating between healthy infants and infants suffering from hypothyroidism based on their cries. The infant cries were first divided into one second segments, and important features were extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. Two methods were then used to select which MFCC coefficients to be used as features for the MLP: direct selection or Fishers Ratio analysis (F-ratio analysis). Their performances were compared with experimental results showing that MLP was able to accurately distinguish between the two cases. The classification performance of MLP trained with F-Ratio analysis is found to be better compared to direct selection method.

Optimized Support Vector Machine for classifying infant cries with asphyxia using Orthogonal Least Square

This paper investigates the effect of optimizing Support Vector Machine, with linear and RBF kernels, on its performance in classifying asphyxiated infant cries, with Orthogonal Least Square. Mel Frequency Cepstrum analysis first extracts feature from the infant cry signals. The extracted features are then ranked in accordance to its error reduction ratio with OLS. SVM with linear and RBF kernel then classify the asphyxiated infant cry from the optimized and non-optimized input feature vector. The classification accuracy and support vector number are used to gauge the performance. Experimental result shows that for both kernels, the OLS-optimized SVM achieve equally high classification accuracy with lower support vector number than the non-optimized one. It is also found that the OLS-SVM with RBF kernel outperformed all other methods with classification accuracy of 93.16% and support vector number of 266.2.

A Comparison of Iris Localization Techniques for Pattern Recognition Analysis

This paper presents a comparison of iris localization techniques namely, Circular Hough Transform (CHT), Daugmans Integro Differential Operator (DIDO) and Circular Boundary Detector (CBD) for localization of iris region. The difference among these three techniques are, CHT employs a segmentation technique to highlight the edges of interest and the circular shape of the iris is detected using the equation of circle, DIDO makes use of integro differential operator for locating the iris and pupil regions while CBD is developed based on the equation of circle by first selecting two points at the iris region: one at the center and the other one at the circumference of the iris. Once this selection has been made, the computation of the outer iris boundary takes place. The same procedure is repeated for the construction of inner iris boundary. The circular iris region can be un-wrapped into rectangular form for the purpose of pattern recognition analysis. The iris localization was conducted on iris images taken from healthy women free from Human Papilloma Virus (HPV). The results show that CBD is able to localize the iris region for all tested iris images while using DIDO and CHT, not all iris images can be localized precisely.

Investigation of Mel Frequency Cepstrum Coefficients parameters for classification of infant cries with hypothyroidism using MLP classifier

Hypothyroidism in infants is caused by insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity due to the enlarged liver, the cry signals are unique and can be distinguished from healthy infant cries. We investigate the usage of the Multilayer Perceptron (MLP) classifier to diagnose infant hypothyroidism. The Mel Frequency Cepstrum Coefficients (MFCC) feature extraction method was used to extract important information from the cry signal itself. This study investigates the number of filter banks and coefficients in MFCC to extract optimal information from infant cry signals, to be classified using MLP. The cry signals were first divided into equal-length segments, and MFCC was used to extract features from them. Tests on the combined University of Milano-Bicocca and Instituto Nacional de Astrofisica datasets yielded MLP classification accuracy of 89.18%, suggesting that the optimal MFCC resolution was obtained using 36 filter banks, and 19 coefficients.

Optimization of MFCC parameters using Particle Swarm Optimization for diagnosis of infant hypothyroidism using Multi- Layer Perceptron

This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (fb) and the number of coefficients (nc) used. These parameters are critical in representation of the features as they affect the resolution and dimensionality of the features. In this paper, the PSO algorithm was used to optimize the values of fb and nc. The MFCC features based on the PSO optimization were extracted from healthy and unhealthy infant cry signals and used to train MLP in the classification of hypothyroid infant cries. The results indicate that the PSO algorithm could determine the optimum combination of fb and nc that produce the best classification accuracy of the MLP.

Orthogonal least square based support vector machine for the classification of infant cry with asphyxia

This paper describes the classification of asphyxiated infant cry using orthogonal least square (OLS) based Support vector machine (SVM). The features of the cry signal were extracted using mel frequency cepstral coefficient analysis and significant features were selected using OLS. SVM with linear and RBF kernels were used to classify the asphyxiated infant cry signals. Classification accuracy and support vector number were computed to examine the performance of the OLS based SVM. The highest classification accuracy (93.16%) could be achieved using RBF kernel, however, with large support vector number.

The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron Neural Network

Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in the MFCC are not removed, the performance of the MLP will be degraded. The use of F-ratio is essential to select the significant features. This paper examines the effect of selecting features using F-ratio on the classification accuracy of the MLP. Results obtained from direct selection of coefficients and selection of coefficients via F-ratio, were compared. It is found that the contribution of F-ratio in the selection of input for the MLP has managed to produce high classification accuracy.