Yazdan Ahmad Qadri ¹, Vineet Kumar ¹

Affiliations
1 Lovely Professional University, Jalandhar-Delhi G.T. Road, National Highway 1, Phagwara, Punjab –144411, IN

Abstract

Objectives: Epilepsy is a neurological disorder that is characterized by occurrence of seizures. The Electroencephalogram (EEG) signals are used as the primary source of data for the study of epilepsy. This study uses Mel Frequency Cepstral Coefficients(MFCC) for early detection of epilepsy in adults. Method: Use of MFCC is a de-facto method of Automatic Speech Recognition (ASR). Extending the use of the same method for EEG signals yields reliable results as the properties of EEG signals resemble the properties of speech signals. The training and test samples were taken from EEG database of the University of Bonn. Using the database a support vector machine was trained and then was used for testing. Findings: The use of MFCC and along with Support Vector Machine (SVM) has an average accuracy of 98.5%. Therefore, an epileptic EEG signal can be detected with a high accuracy. The results reaffirmed the fact that there is a high correlation between the speech signals and EEG signals. The newer methods of ASR may be explored for finer results. There is a significant improvement in accuracy over other methods of epilepsy detection.

Keywords

Automatic speech recognition, Epilepsy detection, MFCC.

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Onkar Singh ¹, Vineet Kumar ¹

Affiliations
1 Department of ECE, Lovely Professional University, Phagwara- 144411, Punjab, IN

Abstract

ECG (Electrocardiogramis a critical non-intrusive clinical instrument for the finding of heart diseases.Thediscovery of cardiovascular arrhythmias is a testing assignment since the little varieties in ECGsignals can’t be recognized decisively by human eye. Heart arrhythmias are ordered utilizing Discrete Wavelet Change (DWT) and Double Tree Complex Wavelet Change (DTCWT) procedure.The DWT highlight set involves measurable components extricated from the sub groups got afterdeterioration of QRS complex signs up to 5 scales while the DTCWT highlight set includes waveletcoefficients removed from the fourth and fifth scale disintegration of QRS complex signs. The twoarrangements of elements are affixed independently by two different components (Maximum and minimum) separated from the QRS complex sign of each cardiovascular cycle. These capabilitiesare autonomously grouped utilizing a Support Vector Machine taking into account back spreadcalculation. In this work, 3 sorts of ECG beat (Normal Sinus Rhythm (N), Atrial Fibrillation (AF) and Supraventricular (S)) are characterized from the 48 records of MIT-BIH arrhythmia database.The trial results show that the DTCWT system groups ECG beats with a general affectability of94.78%.

Keywords

Dual-tree complex wavelet transform (DT-CWT), Electrocardiogram (ECG), Empirical mode decomposition (EMD), Support Vector Machine (SVM).

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Kausav Kumar ¹, Vineet Kumar ¹

Affiliations
1 Department of Electronics and Communication Engineering, Lovely Professional University, Phagwara - 144411, Punjab, IN

Abstract

Digital image watermarking is one of the best techniques available now a day to tackle the problems regarding digital image authentication and its security. There are lots of ways and methods and techniques for digital image watermarking. This paper tried to improvise one of those techniques and comes up with a better and improved way to digital image watermarking. In this paper combination of two techniques: - Discrete Wavelet Transform and Arnold Transform are merged together in such a way that the outcome provides best possible results for invisible watermarking. Via using the new technique similarity ratio of 0.892 and signal-to-noise ratio of 59.11 is achieved. Technique is also measured against some of the common attacks on digital image.

Keywords

Arnold Transform, Discrete Wavelet Transform (DWT), Image Authentication, Invisible Watermarking, Signal-to-Noise Ratio (SNR), Similarity Ratio (SR)

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