Papers by Kannathal Natarajan
© 2004 Natarajan et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim cop... more © 2004 Natarajan et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Background: The EEG (Electroencephalogram) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This work discusses the effect on the EEG signal due to music and reflexological stimulation. Methods: In this work, nonlinear parameters like Correlation Dimension (CD), Largest Lyapunov Exponent (LLE), Hurst Exponent (H) and Approximate...
Group project-based learning is commonly used within engineering curriculum to simulate what is e... more Group project-based learning is commonly used within engineering curriculum to simulate what is expected in the real-world of engineering work – working in teams to collaborate and apply their knowledge and skills to solve real engineering problems. However, many engineering educators face multiple challenges in using group projects effectively. Some of the issues observed include team members not equally contributing fairly and equally to the work, poor student motivation due to less individual accountability, and the worry of uneven acquisition of the expected skills, knowledge, and know-how by team members. In this paper, a systematic approach called the ‘Convergent Collaborative Learning (CCL) model’ is proposed to help educators address the challenges of implementing group projects and, at the same time, build students’ interest and confidence in their own ability to learn and solve engineering problems. The CCL model is developed by adopting collaborative learning approach but...
Biomedical engineering online, Jan 16, 2004
The EEG (Electroencephalogram) is a representative signal containing information about the condit... more The EEG (Electroencephalogram) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This work discusses the effect on the EEG signal due to music and reflexological stimulation. In this work, nonlinear parameters like Correlation Dimension (CD), Largest Lyapunov Exponent (LLE), Hurst Exponent (H) and Approximate Entropy (ApEn) are evaluated from the EEG signals under different mental states. The results obtained show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation. It is found that the measures are significantly lo...
Advances in Cardiac Signal Processing
Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at... more Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier.
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Papers by Kannathal Natarajan