A forecasting tool for prediction of epileptic seizures using a machine learning approach

Asharindavida, Fayas; Hossain, M. Shamim; Thacham, Azeemsha; Khammari, Hédi; Ahmed, Irfan; Alraddady, Fahad; Masud, Mehedi

doi:10.1002/cpe.5111

Cited by 16 publications

(6 citation statements)

References 36 publications

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“…Deep learning and machine learning models are widely used for medical diagnosis [ 4 – 7 ]. We now discuss the related works related to predict the risk of depression.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Noor

Asad

Khan

et al. 2021

Computational Intelligence and Neuroscience

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This paper presents a model to predict the risk of depression based on electrocardiogram (ECG). This proposed model uses a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) autoencoder to predict normal, abnormal, and PVC heartbeats. The RNN model is a deep learning-based model to classify normal, abnormal, and PVC heartbeats. We used the model as a classifier. The model uses a heart rates dataset to predict abnormal and PVC heartbeats. As for the dataset, we have used 5000 ECG samples. The model was trained on a training dataset and validation dataset. After that, it was tested on a test dataset. The model is trained on normal heartbeat rates, so the model can predict any heartbeat rates other than normal. Our contribution here is to build a model that can differentiate between “normal,” “abnormal,” and “risky” heartbeats. Our model predicts “normal” heartbeats with 97.24% accuracy and can predict “PVC” heartbeats with 100% accuracy. Other than the accuracy, we evaluated our model on the training loss graphs. These two types of training loss graphs were evaluated as “normal” versus “risky” and “abnormal” versus “risky.” We have seen great results there as well. The best losses for “normal,” “abnormal,” and “risky” are 5.71, 33.36, and 34.78. However, these results may improve if a larger dataset is used. In studies, it was found that patients suffering from depression may have a different kind of heartbeat than the normal ones. In most cases, it is PVC (Premature Ventricular Contraction) heartbeats. Therefore, the target is to predict abnormal heartbeats and PVC heartbeats.

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“…Deep learning and machine learning models are widely used for medical diagnosis [ 4 – 7 ]. We now discuss the related works related to predict the risk of depression.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Noor

Asad

Khan

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

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“…In [12], the authors proposed an algorithm using cooperative multi-scale CNNs for automatic feature learning of iEEG datasets. Their results point out that the proposed method reached an accuracy score of 84% and an average sensitivity of 87.85%.…”

Section: Related Workmentioning

confidence: 99%

“…However, training such a multi-scale network needs longer hours, increasing, therefore, the model complexity [13]. We believe this research [12] can be extended by merging EEG and ECG data by improving classifiers and applying simplified methods for feature extraction. Recognizing that the EEG can provide indications of a seizure hours before seizure onset, the trend is to focus on analysis beyond one hour before the phase of seizure onset.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Models for Predicting Epileptic Seizures Using iEEG Signals

2022

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Epilepsy is a chronic neurological disease characterized by a large electrical explosion that is excessive and uncontrolled, as defined by the world health organization. It is an anomaly that affects people of all ages. An electroencephalogram (EEG) of the brain activity is a widely known method designed as a reference dedicated to study epileptic seizures and to record the changes in brain electrical activity. Therefore, the prediction and early detection of epilepsy is necessary to provide timely preventive interventions that allow patients to be relieved from the harmful consequences of epileptic seizures. Despite decades of research, the prediction of these seizures with accuracy remains an unresolved problem. In this article, we have proposed five deep learning models on intracranial electroencephalogram (iEEG) datasets with the aim of automatically predicting epileptic seizures. The proposed models are based on the Convolutional Neural Network (CNN) model, the fusion of the two CNNs (2-CNN), the fusion of the three CNNs (3-CNN), the fusion of the four CNNs (4-CNN), and transfer learning with ResNet50. The experimental results show that our proposed methods based on 3-CNN and 4-CNN gave the best values. They both achieve an accuracy value of 95%. Finally, our proposed methods are compared with previous studies, which confirm that seizure prediction performance was significantly improved.

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“…Alzheimer's disease severity is determined by the strength of several EEG bands. As a characteristic for EEG categorization, the Mann-Whitney U test was utilised to distinguish the power [7]. Analysis of phase, correlation synchrony, and granger causality using statistical tests such as the Mann-Whitney U test Alzheimer's disease patients exhibit increased synchronisation in these metrics compared to a healthy individual.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of EEG signals using machine learning and deep learning techniques

Prasath

Lokesh

Krishnakumar³

et al. 2022

ijhs

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Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these impulses induce problems in the individual. Hence clinicians analyze electrical activity of the brain by EEG readings to comprehend the disruptions in the impulses. EEG and its sub bands depict electrical pattern of human brain. EEG data comprises transient components, spikes, other sorts of artifacts due to eye blinking, movement of the individual, anxiousness etc. during EEG collection. Wavelet transformations are effective mathematical technique for sampling approximation to produce clear EEG. It also assists in filtering, sampling, interpolation, noise reduction, signal approximation and signal augmentation, feature extraction. In this study, a survey is done on EEG motor imagery signals and the various classifiers to assess them machine learning methods for EEG signal categorization are studied. Conventional SVM and logistic regression methods combined with basic 2-layer Neural Network (NN) are constructed using python in keras to assess the performances.

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A forecasting tool for prediction of epileptic seizures using a machine learning approach

Cited by 16 publications

References 36 publications

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

Deep Learning Models for Predicting Epileptic Seizures Using iEEG Signals

Classification of EEG signals using machine learning and deep learning techniques

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