Informatics Publishing Limited

S. Suja Priyadharsini ¹, S. Edward Rajan ², S. Saranya ³

Affiliations
1 Department of Electronics and Communication Engineering, Anna University of Technology, Tirunelveli, Tamil Nadu, IN
2 Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, IN
3 Department of Electronics and Communication Engineering, Anna University Tirunelveli, Tirunelveli, Tamil Nadu, IN

Abstract

The Fetal Electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart. It contains information about the health status of the fetus and as a result, an early diagnosis of any cardiac defects before delivery increases the effectiveness of the appropriate treatment. The proposed approach extracts the FECG from two ECG signals recorded at the thoracic and abdominal areas of the mother, with the help of a hybrid soft computing technique. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite because it contains both the maternal and the fetal ECG signals. The principle used for the elimination of artifacts is ANC. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to remove the artifacts and to extract the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratio. After removing the artifacts using ANFIS, better results are obtained by optimizing the ANFIS parameters using Swarm Intelligent Technique, namely Particle Swarm Optimization (PSO). The experimental results show that the proposed approach can effectively remove artifacts and extract the desired FECG signals from the abdominal signals.

Keywords

Electrocardiogram (ECG), Adaptive Neuro-Fuzzy Inference System (ANFIS), Fetal ECG (FECG), Maternal ECG (MECG), Particle Swarm Optimization (PSO).

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M. Sorna Suguna ¹, S. Suja Priyadharsini ²

Affiliations
1 Anna University, Tirunelveli, IN
2 ECE Department, Anna University, Tirunelveli, IN

Abstract

Iris recognition is one of the most promising approaches in the area of biometric due to its high reliability for personal identification. The proposed approach provides an efficient iris recognition algorithm using phase-based image matching that is,an image matching technique using only the phase components in 2D Curvelet Transform of given images. This approach supports proper preprocessing steps to remove the irrelevant parts correctly from the given image and to extract only the iris region. The phase codes are generated by using curvelet transform from the extracted iris region. The phase based image matching technique provides a  unified framework for high accuracy biometric authentication. Matching is done by block partitioning method. The phase only correlation(POC) matching algorithm is proposed for this approach. By using this matching algorithm by introducing a spatial ensemble averaging of the POC function is suitable for the degraded iris images.

Keywords

Biometrics, Curvelet Transform, Iris Recognition, Phase-Based Image Matching, Phase-Only Correlation.

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Jenisha J. Hannah ¹, S. Suja Priyadharsini ¹

Affiliations
1 Anna University of Technology, Tirunelveli, IN

Abstract

Automated electrocardiogram (ECG) signal processing and accurate beat classification is of high need in clinical applications. A repetition detection approach is employed to create an adaptive profile for each person according to his cardiac behaviour. Heart arrhythmia are characterised by variations in the heart rate and irregularity. The key novelty of this approach is twofold. A technique using wavelet analysis with adaptive thresholding is employed to accurately extract the QRS complexes of an ECG signal. Next the patient adaptive profiling scheme is implemented to derive the cardiac profile specific to an individual. As ECG morphologies vary from person to person and from conditions to conditions an adaptive ECG profile is very much needed. This technique clearly identifies a normal region for a person and can thus identify abnormal beats that fall outside this region. The multilayer perceptron back propagation neural network is then combined which acts as a global classifier for enhanced classification performance.

Keywords

Electrocardiogram (ECG), Repetition Detection, Wavelet, Adaptive ECG Profile, Neural Network.

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R. Muthusivagami ¹, S. Suja Priyadharsini ¹

Affiliations
1 Department of Electronics and Communication Engineering, Anna University of Technology, Tirunelveli, Tamil Nadu, IN

Abstract

This paper resolves the difficult superimpositions of Motor Unit Action Potentials (MUAPs) measured from Electromyographic Recordings using Improved Particle Swarm Optimization (IPSO) algorithm. The contraction of muscles depend on the activity of motor neurons (MNs) each giving rise to Motor Unit Action Potentials (MUAPs). Since Motor Unit (MU) firings are asynchronous, MUAPs inevitably overlap from time to time over the course of recording, making them difficult to locate. Resolution of these superimposed potentials into individual constituents is a combinatorial optimization problem which requires determining of their individual MUAPs as well as their relative time shifts. PSO is a new random computational method for tackling optimization functions. However, it is easily trapped into local optimum when solving the difficult superimposition, which makes the performance of PSO greatly reduced. To overcome this shortcoming, this paper proposes Improved Particle Swarm Optimization (IPSO) algorithm, which uses the third particle, guides the current particles’ velocity updating rule and reduces the probability of trapping into the local optimization. Thus the proposed method highly reduces the time taken to resolve difficult superimposition and gives 99.8% accuracy in simulation study. Numerical results shows that the proposed algorithm has great performance in accuracy and the results are compared with conventional particle swarm optimization.

Keywords

Electromyography (EMG), Motor Unit Action Potentials (MUAPs), Superimposition, Improved Particle Swarm Optimization (IPSO).

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