Informatics Publishing Limited

S. Muthu Meena ¹, K. Vimala ², V. Kalaivani ¹

Affiliations
1 National Engineering College, Kovilpatti, Tamil Nadu, IN
2 National Engineering College, Kovilpatti, Tamil Nadu, IN

Abstract

Human emotional assessment helps in identification of human stress, which is detected using multi-modal bio-signals. Multi-modal bio-signal involves EEG signals and psycho-physiological signals such as Skin Conductance (SC), Blood Pressure (BP), Heart Rate Variability, and Respiration. The raw EEG signal and psycho-physiological signals were pre-processed and decomposed into five different frequency bands (delta, theta, alpha, beta, gamma) using Discrete Wavelet Transform (DWT). In this work, we used two different wavelet functions namely db8 and sym8 for extracting the statistical features from EEG signal and psycho-physiological signals for classifying the emotional stress. In order to evaluate the efficiency of emotional stress, Support Vector Machine (SVM) is used.

Keywords

Electroencephalogram (EEG), Emotional Stress Assessment, EEG Signal, Psycho-Physiological Signals, Discrete Wavelet Transform, Support Vector Machine (SVM).

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M. Divya ¹, V. Kalaivani ², V. Anusuya Devi ³

Affiliations
1 Department of Computer Science and Engineering–PG, National Engineering College, Kovilpatti, Tamilnadu, IN
2 Department of Computer Science and Engineering – PG, National Engineering College, Kovilpatti, Tamilnadu, IN
3 Department of Computer Science and Engineering – PG, National Engineering College, Kovilpatti, Tamilnadu, IN

Abstract

In this paper, we propose a novel method for the detection of heart diseases. It is proposed to develop an automated system for the classification of heart diseases. The proposed system includes pre-processing, peak detection, feature extraction, feature selection and classification. In pre-processing, the noise removal is done and then peak detection of input ECG signal is performed. The peak detection process is used to detect the peaks in the ECG signal. It is for the detection of QRS complex, QRS interval, from the ECG signal. Then, many time domain and frequency domain features are extracted and some among them are selected for the classification of heart diseases. This proposed system may be helpful for the clinical diagnosis of heart diseases like Ventricular Arrhythmias, Atrial Fibrillation and Atrial flutter.

Keywords

Electrocardiogram, ECG Signals, Heart Diseases, QRS Complex, Peak Detection.

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R. Lakshmi Devi ¹, V. Kalaivani ¹

Affiliations
1 Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Nallatinputhur – 628503, Tamil Nadu, IN

Abstract

Objectives: Cardiac arrhythmia is a type of disorder where the heartbeat is irregular, too slow, or too fast. As a result of heart diseases, there is an increase in death yearly. The early detection of cardiac diseases is important for preventing the deaths due to the cardiac diseases. Methods: The Electrocardiogram (ECG) is used to record the electrical activity of the heart for physician to diagnose the heart diseases. In this study, we propose a cardiac diagnosis system for diagnosing cardiac arrhythmia disease. It will be most helpful for the patients who undergone a heart surgery for continuous monitoring of post-surgical status. Findings: The major objective of this paper is to implement an effective algorithm to discriminate between the normal and diseased persons. The Monitoring process includes the following tasks, such as preprocessing and feature extraction by Pan Tompkin’s algorithm and the features are classified using neural network and support vector machine. The performance of the classifiers was evaluated using the parameters such as sensitivity, specificity and accuracy. The Accuracy of the neural network algorithm is 88.54% and the accuracy of the Support Vector Machine is 84.37%. Application/Improvements: The Neural network classifier shows better performance compared to support vector machine. In future the classifier is trained using the best set of features using feature selection techniques. *

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