DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

Jana, Gopal Chandra; Agrawal, Anupam; Pattnaik, Prasant Kumar; Sain, Mangal

doi:10.3390/diagnostics12020324

Cited by 16 publications

(5 citation statements)

References 42 publications

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“…Instantaneous energy, Teager energy, HFD, and PFD were extracted from each decomposed IMF and wavelet. Alternatively, in [ 89 ], several statistical features were extracted. Combining both EMD and DWT features yielded more information about the signal, resulting in better seizure classification.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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“…One such pre‐processing method used very extensively for filtering unwanted noise and extracting the region of interest from non‐stationary signals is Empirical Mode Decomposition (EMD). EMD (M. U. Khan et al, 2021; M. U. Khan et al, 2019) has been extensively used in biomedical signals analysis and processing for extracting the region of interest and removing noise (Aziz et al, 2021; Iqtidar et al, 2021) and for extracting features (Jana et al, 2022). In Empirical Mode Decomposition (EMD) our original noisy signal is decomposed into multiple resolutions or modes called the Intrinsic Mode Functions (IMFs).…”

Section: Methodsmentioning

confidence: 99%

“…All operations were performed, and all results were obtained on MATLAB R2021a software running on an Intel Core i5 computer with 8 GB of RAM. and removing noise (Aziz et al, 2021;Iqtidar et al, 2021) and for extracting features (Jana et al, 2022). In Empirical Mode Decomposition (EMD) our original noisy signal is decomposed into multiple resolutions or modes called the Intrinsic Mode Functions (IMFs).…”

Section: Methodsmentioning

confidence: 99%

Machine learning‐based classification of multiple heart disorders from PCG signals

et al. 2023

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Timely and accurate detection and diagnosis of heart disorders is a significant problem in the medical community since the mortality rate is increasing. Pulsing of cardiac structures and blood turbulence creates heart sounds recorded and detected through Phonococardiogram (PCG). As a non‐invasive technique, PCG signals have a strong ability to be used for designing automatic classification of possible heart disorders. This paper presents an expert system design for the detection and classification of PCG signals for five classes, namely, healthy, aortic stenosis, mitral stenosis, mitral regurgitation, and mitral valve prolapse. In this work, a single‐channel PCG signal is first decomposed using Empirical Mode Decomposition (EMD) into different modes known as intrinsic mode functions (IMFs). Manual signal analysis is applied to identify the relevant IMFs to construct a preprocessed signal. We proposed an automated energy‐based signal reconstruction through IMFs. The proposed algorithms automatically identify the relevant IMFs and added them together to form a preprocessed signal. After preprocessing, the first nine features of Mel Frequency Cepstral Coefficients (MFCC) were computed and passed to several classification methods such as Fine Tree, Quadratic Discriminant, Kernel Naive Bayes, Support Vector Machines (SVM), Fine K‐Nearest Neighbours (Fine‐KNN), Ensemble Bagged Trees and Neural Network. The best performance of 99.3% accuracy was obtained via Fine‐KNN using 10‐fold cross‐validation. The proposed method was evaluated on a publicly available dataset of heart sounds. The proposed method demonstrated improved performance as compared to the existing state‐of‐the‐art methods.

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“…Nowadays, multichannel EEG signal detection is commonly used to improve the accuracy of seizure detection. For multichannel EEG signals, Jana et al [ 13 ] studied the effectiveness of discrete wavelet transform (DWT) and EMD feature fusion on four different classifiers for seizure detection, and finally, the accuracy and F1-score reached more than 90%. Moreover, Diykh et al [ 14 ] proposed a wavelet-based texture approach to detect seizures.…”

Section: Related Workmentioning

confidence: 99%

Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet

Zhang

Liu

2022

Journal of Healthcare Engineering

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Automatic seizure detection technology has important implications for reducing the workload of neurologists for epilepsy diagnosis and treatment. Due to the unpredictable nature of seizures, the imbalanced classification of seizure and nonseizure data continues to be challenging. In this work, we first propose a novel algorithm named the borderline nearest neighbor synthetic minority oversampling technique (BNNSMOTE) to address the imbalanced classification problem and improve seizure detection performance. The algorithm uses the nearest neighbor notion to generate nonseizure samples near the boundary, then determines the seizure samples that are difficult to learn at the boundary, and lastly selects seizure samples at random to be used in the synthesis of new samples. In view of the characteristic that electroencephalogram (EEG) signals are one-dimensional signals, we then develop a 1D-MobileNet model to validate the algorithm’s performance. Results demonstrate that the proposed algorithm outperforms previous seizure detection methods on the CHB-MIT dataset, achieving an average accuracy of 99.40%, a recall value of 87.46%, a precision of 97.17%, and an F1-score of 91.90%, respectively. We also had considerable success when we used additional datasets for verification at the same time. Our algorithm’s data augmentation effects are more pronounced and perform better at seizure detection than the existing imbalanced techniques. Besides, the model’s parameters and calculation volume have been significantly reduced, making it more suitable for mobile terminals and embedded devices.

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DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

Cited by 16 publications

References 42 publications

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Machine learning‐based classification of multiple heart disorders from PCG signals

Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet

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