A Real-Time Epilepsy Detection Method Using Embedded Zero Tree Wavelet Approach and Support Vector Machine P. Padmapriya, V. Rajamani Behavioural Neurology, 2025 Temporary disturbances in brain function are caused by epilepsy, a chronic disorder resulting from sudden abnormal firing of brain neurons. This research introduces an innovative real‐time methodology representing detecting epileptic spasms from electroencephalogram (EEG) data. It employs a support vector machine (SVM) alongside embedded zero tree wavelet (EZW) transform. To facilitate precise multiresolution analysis of epileptic convulsions, the EZW method is selected for its capacity to efficiently compress multichannel EEG data while preserving crucial diagnostic features. EZW effectively captures and encodes key patterns in EEG signals, enabling detailed analysis of the subtle variations associated with seizures. This study extracts statistical features such as entropy, kurtosis, skewness, and mean from the compressed EEG segments. These features are then classified using the SVM to distinguish between normal and epileptic states. With a remarkable 99.02% classification accuracy and a false positive rate of only 1.1%, the proposed algorithm demonstrates excellent performance. The novelty lies in integrating SVM with EZW‐based feature extraction and advanced preprocessing, enabling efficient real‐time EEG analysis. Unlike previous works, this approach preserves critical information, enhances classification accuracy, and supports multichannel signals, offering a robust and practical solution for real‐time epilepsy detection. Based on these findings, the method is considered highly suitable for real‐time implementation in clinical environments.
A Novel Automated Solution for Biomedical Waste Management P. Padmapriya, D. Banupriya, J. Saminathan, John William, Manoharan Loganathan, R. Pavithra Journal of Environmental Nanotechnology, 2025 Efficient management of biomedical waste is essential for ensuring public health and environmental safety. With the growing volume of biomedical waste, effective segregation methods are increasingly necessary. This project focuses on developing an automated biomedical waste segregation device utilizing Arduino technology and sensors. The device categorizes biomedical waste into three distinct types: dry, wet, and metal. An infrared motion sensor identifies the type of waste based on preprogrammed characteristics, while an LCD provides real-time updates on the segregation status. Extensive testing with various types of biomedical waste has demonstrated the device's ability to accurately and efficiently allocate waste to the appropriate bins. Leveraging Arduino and sensor technologies, this system offers a cost-effective and practical solution for biomedical waste segregation. This innovation holds particular promise for addressing waste management challenges in developing countries, where effective biomedical waste handling is a pressing issue. In summary, this project illustrates the potential of using Arduino-based systems for biomedical waste segregation. The device has proven to be reliable, efficient, and economical, offering significant benefits for both public health and environmental safety.
A Biometric-Finger Vein Authentication System for Security Purpose using Deep Learning Technique S. Sathishkumar, P. Padmapriya, Praveen Kumar Thabjul, Shanmuganathan Raghavan 2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023 Biometrics uses human physiological characteristics, is one way of protecting personal information. The usage of finger vein as a form of biometric become most popular method recently. Finger vein authentication provides a high level of security and accuracy, making it a reliable biometric authentication method. Finger vein authentication system compares the vascular structure of a person's finger with previously acquired data. This technique involves identifying patterns in vein images of human fingers below the skin's surface. The proposed system aims to enhance the security of user authentication by utilizing the unique features of finger vein patterns. The finger vein image is acquired from the database. The preprocessing done in order to remove the noise by means of Gaussian median filter in spatial domain and frequency domain. The segmentation of the image carried out through line tracking method which provided the better contrast image. The system utilizes Convolutional Neural Networks for feature extraction and the features are matched with the finger vein database. Then Deep Learning Approach used for classification of finger vein patterns between genuine and imposter users. For real-time communication the scanner scans the finger vein and send to Arduino board for storage followed by MATLAB for processing and classification of the images. The result is sent through GSM module as alarm or message. Then the information also stored in IoT for future references. with the user a GSM Module is integrated. The proposed system gives an accuracy of 96%. Thus, system is beneficial in several security applications such as access control, identity verification, banking system, and financial transactions.
Skin cancer detection using non-invasive techniques Vigneswaran Narayanamurthy, P. Padmapriya, A. Noorasafrin, B. Pooja, K. Hema, Al'aina Yuhainis Firus Khan, K. Nithyakalyani, Fahmi Samsuri Rsc Advances, 2018 Recent advances in non-invasive techniques for skin cancer diagnosis.
Surface modification of titanium with chitosan extraction from crab shell by spin coating International Journal of Control Theory and Applications, 2016
Detection and classification of brain tumor using Radial Basis Function P. Padmapriya, K. Manikandan, K. Jeyanthi, V. Renuga, J. Sivaraman Indian Journal of Science and Technology, 2016 Aim: This paper proposes the automatic support system for detecting the tumor cells by analyzing the scalp EEG by means of RBF technique. Objectives: To acquire the EEG signal from the various electrodes. The artificial neural network will be focused to split up the EEG signal whether cyst i.e. tumor or regular. Methods: The EEG signal is been acquired from the subject using EEG scalp electrodes. The various features such as mean, variance, co-variance, Eigen values and Eigen vectors are extracted from those signals using the Principal Component Analysis. Radial Basis Function (RBF) networks are feed-forward networks which uses a supervised training algorithm are used for function approximation, time series prediction and system control. The RBF is used to train and classify the signal whether the subject is normal or suffering from abnormalities. Results: The features are been extracted using the Principal Component Analysis and the features are skilled. Thus the acquired signals are been classified as cyst or normal. Conclusion: Thus in this paper an automatic system is been developed for diagnosing the tumor cells by means of analyzing EEG signal which is non-invasive method. It can also extend for analyzing other diseases seizures of epilepsy, Alzheimer's disease.
Design and development of a foot pressure scanner for diabetic patients K. Manikandan, Logesh Kumar, J. Sivaraman, P. Padmapriya, C. Vijayalakshmi Indian Journal of Science and Technology, 2016 Objective: The purpose of the study is to develop a system for measuring the pressure level at several points on the foot sole. Methodology: The pressure sensors placed on the pressure plate develops a voltage for the corresponding exerted pressure and the voltages are amplified to a considerable value and fed into the multiplexer for digitization by using A/D convertor for further processing. All the data are normalized to obtain an appropriate image. The image colour intensities indicate different pressure distributions of the foot. Findings: The diabetes patients are made to walk on the pressure device and the distributions of pressure are simulated with various color coding using Visual Basic (VB) and the range of pressure from minimum to maximum is validated. Applications/Improvements: The pressure distribution of a patient is found in the foot sole and an appropriate foot wear can be designed to avoid foot ulcers and other deformations.
Blood microscopic image analysis for acute leukemia detection International Journal of Control Theory and Applications, 2016
