ROC Analysis of EEG Subbands for Epileptic Seizure Detection using Naïve Bayes Classifier

Sameer, Mustafa; Gupta, Bharat

doi:10.13052/jmm1550-4646.171315

Cited by 19 publications

(4 citation statements)

References 24 publications

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“…Image segmentation is a critical step in the image analysis process. The purpose of developing a Convolutional Neural Network for segmentation is to use more meaningful information to improve lung segmentation [28]. The promise of better performance in general automatic lung segmentation systems, which are crucial for a variety of medical and scientific applications, has encouraged this.…”

Section: System Modelmentioning

confidence: 99%

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

Sangeetha¹,

Afreen²,

Ahmad³

2021

RCES

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Lung infection or sickness is one of the most common acute ailments in humans. Pneumonia is one of the most common lung infections, and the annual global mortality rate from untreated pneumonia is increasing. Because of its rapid spread, pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global danger as of December 2019. At the clinical level, the COVID-19 is frequently measured using a Computed Tomography Scan Slice (CTS) or a Chest X-ray. The goal of this study is to develop an image processing method for analysing COVID-19 infection in CT Scan patients. The images in this study were preprocessed using the Hybrid Swarm Intelligence and Fuzzy DPSO algorithms. According to extensive computer simulations, the persistent learning strategy for CT image segmentation using image enhancement is more efficient and adaptive than the Medical Image Segmentation (MIS) method. The findings suggest that the proposed method is more dependable, accurate, and simple than existing methods.

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Section: System Modelmentioning

confidence: 99%

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

Sangeetha¹,

Afreen²,

Ahmad³

2021

RCES

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“…Naive Bayes (NB): This ML algorithm is based on the Bayes theorem, the main idea is that we calculate the probability of each type if that item will appear in that condition or not, of probability is large that item will be classified in that category. Reference [36] used image descriptors for epileptic seizure detection, in this methodology Short-time Fourier transform (STFT) was used to transform EEG Signals into images, the extracted feature matrix was used as an input for Naïve Bayes (NB) classifier which gave an accuracy of 98%, which was significantly better (Fig. 5).…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Sachadev

Bhatnagar

2021

Studies in Computational Intelligence

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Medical images play an important role in the diagnosis of diseases effectively. Human brain is consisting of millions of neurons which work in proper coordination with one another, and human behavior is an outcome of the response of neurons to internal/external motor or sensory stimuli. These neurons are the carriers of signals from different parts of the human body and the brain. Human cognition studies focus on interpreting either these signals or brain images and there are various technologies which are in use for brain disease studies. Every image or technology generates lot of data in different form which can be modeled using Artificial Intelligence models. The electroencephalogram (EEG) is a recording of the electrical activity of the brain from the scalp. The recorded waveforms reflect the cortical electrical activity. Signal intensity: EEG activity is quite small, measured in microvolts (mV). Delta, Theta, Alpha and Beta are the main frequencies of human EEG waves and EEG electrodes are used to generate EEG plots. In this chapter, authors have tried to capture details on various technologies, techniques, usage of AI models in Brain mapping for disease predictions. This chapter presents a comprehensive account on them and will appeal to researchers in the field of human cognition.

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“…Most of the automated system presented by the researchers is based on handengineered features extraction and feature selection techniques (Dhiman et al, 2021). Among various available methods, timefrequency (t-f) (Sameer and Gupta, 2020a;Tzimourta et al, 2019;Tzallas et al, 2009;Sameer and Gupta, 2021) analysis is widely used for the extraction of features. Discrete wavelet transform and Short-time Fourier transform (STFT) (Sameer et al, 2020b) are widely using techniques under t-f analysis.…”

Section: Related Workmentioning

confidence: 99%

Automatic epileptic seizure detection using LSTM networks

Shekokar¹,

Dour

2021

WJE

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Purpose The purpose of this work is to make a computer aided detection system for epileptic seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more unprovoked seizures. The common and significant tool for aiding in the identification of epilepsy is Electroencephalography (EEG). The EEG signals contain information about the electrical activity of the brain. Conventionally, clinicians study the EEG waveforms manually to detect epileptic abnormalities, which is very time-consuming and error-prone. Design/methodology/approach The authors have presented a three-layer long short-term memory network for the detection of epileptic seizures. Findings The network classifies between seizure and non-seizure with 99.5% accuracy only in 30 epochs. This makes the proposed methodology useful for real-time seizure detection. Originality/value This research work is original and not plagiarized.

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ROC Analysis of EEG Subbands for Epileptic Seizure Detection using Naïve Bayes Classifier

Cited by 19 publications

References 24 publications

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Automatic epileptic seizure detection using LSTM networks

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