Secured Home Automation with Encrypted Fast Fourier Transformation

Dadi, Venkatarao; Pathakamuri, Naresh; Kolluri, Satya Venu Abhishek; Suryadeep, K.; Gurugubelli, Satya Tarun; Mandala, Prabhu Teja

doi:10.1007/978-981-16-8862-1_58

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Further on, both matrices are passed to wavelet filter that decompose the signals. Although, there exist numerous methods for signals feature extraction such as Fast Fourier Transformation [15] Eigenvectors and Time-Frequency Distributions [16]. However, wavelet transformation (WT) is considered to be the most optimal method due to its key role in diagnostic field and its noncomplex compression of data points in biomedical signals.…”

Section: Features Extractionmentioning

confidence: 99%

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Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

Mohammad¹,

Al-Ahmadi²

2023

Computers, Materials &Amp; Continua

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There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI's the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis. Their performance may sometime degrade due to missing information, selection of poor classification model and unavailability of quality data that are used to train the models for better prediction. This research work is an attempt to epileptic seizures detection by using a multi focus dataset based on EEG signals and brain MRI. The key steps of this work are: feature extraction having two different streams i.e., EEG using wavelet transformation along with SVD-Entropy, and MRI using convolutional neural network (CNN), after extracting features from both streams, feature fusion is applied to generate feature vector used by support vector machine (SVM) to diagnose the epileptic seizures. From the experimental evaluation and results comparison with the current state-of-the-art techniques, it has been concluded that the performance of the proposed scheme is better than the existing models.

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Section: Features Extractionmentioning

confidence: 99%

“…The traditional calculation for each measure has been presented in Eqs. (15) to (18). A confusion matrix has some effects on prediction over a classification problem such as the number of predictions that are right or wrong are categorized by class with count values that leads to matrix uncertainty.…”

Section: Confusion Matrixmentioning

confidence: 99%

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

Mohammad¹,

Al-Ahmadi²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Secured Home Automation with Encrypted Fast Fourier Transformation

Cited by 1 publication

References 14 publications

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

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