RETRACTED ARTICLE: Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier

Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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“…When dealing with real-time and noisy data, the reliefF algorithm is favourable. The reliefF algorithm selects the instances

at random and then determines the k nearest neighbours in the similar and dissimilar classes [ 42 ]. These instances are referred to as

for nearest hit and miss.…”

Section: Methodsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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“…The larger the feature weights, the better its classification performance. The ReliefF algorithm [ 8 ] is described in Algorithm 1 .…”

Section: Relevant Theory and Methodsmentioning

confidence: 99%

“…The results showed that the method was able to significantly reduce the number of channels while ensuring a certain classification accuracy. Praveena et al [ 8 ] proposed a supervised classifier-based important feature selection method for seizure recognition, in which the ReliefF method was used to reduce the dimensionality of extracted features, and the long short-term memory (LSTM) method was used for classification. The results showed that the classification accuracy of the method was improved by 0.6%-16%.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

Liao

Shangguan

Peng

et al. 2022

Computational and Mathematical Methods in Medicine

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Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

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“…From Table 5, it can be analysed that the present study outperforms all other existing studies in terms of all the performance metrics. DWT to obtain an accuracy of 82.15%; (Roy et al, 2019) obtained f1-score of 72.3% using the ResNet50 model; (Raghu et al, 2020) successfully employed the concept of transfer learning to obtain the features using 10 pre-trained networks and combined them with SVM to obtain the highest accuracy of 88.30% from GoogleNet based features; and (Praveena et al, 2021) achieved accuracy, specificity and sensitivity of 98.78%, 99.70% and 99.80% respectively using a combination of handcrafted features and LSTM (Asif et al, 2020).…”

Section: In Terms Of Reliability Using Cohen's Kappa Metricmentioning

confidence: 99%

“…Most of the authors have explored a variety of deep learning based architecture models for current multi‐class classification problem. (Tang et al, 2019) computed the spatio‐temporal dependencies from the graph convolutional Recurrent Neural Network (RNN) for classification of EEG signals and achieved f1‐score, precision and sensitivity of 62%, 63% and 62% respectively; (Sriraam et al, 2019) employed VGG16, VGG19, AlexNet and basic CNN to obtain the highest accuracy of 84.06% from AlexNet based classification framework; (Jiang et al, 2019) combined inductive transfer learning based approach with Discrete Wavelet Transform (DWT) to obtain an accuracy of 82.15%; (Roy et al, 2019) obtained f1‐score of 72.3% using the ResNet50 model; (Raghu et al, 2020) successfully employed the concept of transfer learning to obtain the features using 10 pre‐trained networks and combined them with SVM to obtain the highest accuracy of 88.30% from GoogleNet based features; (Praveena et al, 2021) achieved accuracy, specificity and sensitivity of 98.78%, 99.70% and 99.80% respectively using a combination of handcrafted features and Long‐Short Term Memory (LSTM); and (Asif et al, 2020) proposed SeizureNet framework in which an ensemble of deep learning based CNN models classified the EEG based saliency‐encoded spectrograms to obtain a f1‐score of 98%.…”

Section: Related Workmentioning

confidence: 99%

EEG‐based automatic multi‐class classification of epileptic seizure types using recurrence plots

2021

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There is an urgent need to develop an efficient system for accurate recognition of epileptic seizure type that could play a significant role in reducing the adversial effects of the disease. A lot of work is available for EEG based automatic seizure detection but very less attempts have been made towards the classification of variants of seizures. Moreover, none of the authors have included the EEG signals for myoclonic seizure type in their classification studies. Our study aims to propose the automatic machine learning based multi-class classification system for classifying six types of epileptic seizures (such as absence, focal non-specific, complex-partial, generalized, tonic-clonic and including myoclonic too) through the use of recurrence plots (RPs). In our study, we have collected 19-channel EEG data from a huge database of Temple University Hospital. Two classification modalities have been proposed depending upon the feature extraction approach followed for RPs-(a) Traditional approach using the recurrence quantification analysis (RQA) method;(b) Texture based approach using the hybrid method named as Unthresholded Recurrence Plot with Fractal Weighted Local Binary Pattern (URP-FWLBP), that has been proposed in our work using a combination of Unthresholded RPs (URPs) and Fractal Weighted Local Binary Pattern (FWLBP) method. Thereby, an indirect variant of support vector machine (one-vs-rest approach) has been used as the multi-class classifier in both the approaches. The performance of the proposed modalities has been validated using five-fold cross-validation method in the light of seven metrics-accuracy, sensitivity, specificity, Matthews correlation coefficient, geometric-mean, precision and f1-score. The experimental results show the effectiveness of the proposed system following hybrid URP-FWLBP method in performing multi-class epileptic seizure type classification with 100% efficiency, thereby outperforming the traditional RQA method based system as well the existing state-of-the-art systems.

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RETRACTED ARTICLE: Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier

Cited by 21 publications

References 45 publications

A Proposed Framework for Early Prediction of Schistosomiasis

A Proposed Framework for Early Prediction of Schistosomiasis

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

EEG‐based automatic multi‐class classification of epileptic seizure types using recurrence plots

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