Eye State Detection from Electro-Encephalography Signals using Machine learning Techniques

Dutta, Kusumika Krori; Manohar, Premila; Rajagopalan, Shwethaa; Naaz, Falak; Lakshminarayanan, Meenakshi

doi:10.1109/mysurucon55714.2022.9972567

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Cytological diagnosis [13], But artificial intelligence has opened new paradigm for prediction and diagnosis of these diseases. Various disease like epileptic seizure detection [14], cardiac condition prediction, lung cancer stages, disease prediction from frequency of eye blinking [15] etc. Using convolutional neural networks (CNN) and transfer learning for lung cancer detection [16], algorithm for identifying lung nodules based on deep feature fusion [17], Classification of Lung Disease Using a Deep Learning Algorithm Based on Voting [18] etc.…”

Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

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Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…In [22]- [24] used a combination of seven different classifiers, namely SVM, decision tree (DT) the fuzzy surgeon classifier (FSC), KNN, Gaussian mixture models (GMM), probabilistic neural network, and nave Bayes (NB), to differentiate between a patient's three stages of "normal", "preictal", and "ictal". With preprocessed data, several classifiers were used in [25], [26] namely KNN, SVM, a polynomial classifier, logistic model tree (LMT), an uncorrelated normal density-based classifier (UDC), DT.…”

Section: Introductionmentioning

confidence: 99%

Seizure stage detection of epileptic seizure using convolutional neural networks

Krori Dutta,

Manohar,

Krishnappa

2024

IJECE

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

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Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals

Kaushik,

Balachandra,

Olivia

et al. 2024

Int. j. inf. tecnol.

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Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes.

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Eye State Detection from Electro-Encephalography Signals using Machine learning Techniques

Cited by 3 publications

References 21 publications

Supervised learning techniques for detection of Lung Carcinoma

Supervised learning techniques for detection of Lung Carcinoma

Seizure stage detection of epileptic seizure using convolutional neural networks

Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals

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