Automatic detection of epileptic seizure events using the time-frequency features and machine learning

Zeng, Jiale; Tan, Xiaodan; Zhan, Chang’an A.

doi:10.1016/j.bspc.2021.102916

Cited by 17 publications

(13 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main difference between EWT and DWT is that EWT decomposes the signal using adaptive frequency boundaries for each wavelet based on the information content, in contrast to DWT which uses fixed frequency bands [ 74 ]. In [ 75 , 76 ], experiments involving DWT and EWT have shown that the adaptivity of EWT slightly improves seizure detection since the DWT’s fixed filter band efficiency is limited. Although EWT showed a significant improvement, its performance was still restrained by noisy and highly non-stationary signals [ 77 ].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the procedure may be required in some applications such as wearable devices where using a large number of channels is impractical [ 26 ]. Channel selection can be performed using different approaches, whether they are statistical approaches [ 11 , 22 , 36 , 62 , 75 , 76 , 113 ], data-driven approaches [ 14 , 88 , 116 , 117 , 118 ], wrapper approaches [ 119 ], or from prior knowledge based on previous studies [ 120 , 121 ].…”

Section: Discussionmentioning

confidence: 99%

“…Following that, MI between the seed channel and the remaining channels is evaluated, and the channels with the highest similarity to the seed channel are selected. According to [ 75 ], calculating the kurtosis of all the channels and selecting the highest values is a better alternative since the former method implies that the channel with the lowest SD has the highest SNR. Nevertheless, this can also indicate that this channel contains very few seizure events.…”

Section: Discussionmentioning

confidence: 99%

“…Synthetic minority oversampling technique (SMOTE) [ 133 ] is a technique that generates new samples by determining the k-nearest neighbors of every minority class sample, then randomly creating new linearly correlated samples between the designated sample and its neighbors. In [ 11 , 75 , 76 ], SMOTE was used to generate new samples and balance the dataset. An adaptive synthetic algorithm (ADASYN) [ 128 ] is similar to SMOTE, with a primary difference being that it takes into account the local distribution of the class to be oversampled and scatters the new samples by adding some variation to them.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nafea

Ismail

2022

Bioengineering

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nafea

Ismail

2022

Bioengineering

View full text Add to dashboard Cite

show abstract

“…However, the pipeline method cannot consider the argument information when performing trigger word recognition, which affects the argument recognition, so a joint learning method is proposed. The joint learning method solves the cascading error of the pipeline method, and builds a joint learning model by identifying trigger words and arguments at the same time, so that the information of trigger words and arguments can mutually promote the extraction effect [ [12]]. Although the method of machine learning does not depend on the content and format of the corpus, it requires a large-scale standard corpus.…”

Section: Related Workmentioning

confidence: 99%

Small sample medical event extraction based on data augmentation

Li²,

Li³

et al. 2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

View full text Add to dashboard Cite

The key problem of event extraction in the medical field is that the cost of medical data labeling is too high, and the labeled samples are scarce, making medical event extraction difficult. In response to this problem, this paper proposes to perform a partial synonymous replacement of training samples to expand the data, and the data and the original data together constitute new electronic medical record data (i.e. EDA data enhancement); In addition, the unlabeled data is predicted by the medical event extraction model to generate labeled data, and then the accurate labeled data is filtered out and added to the original data to form new electronic medical record data, thereby realizing data enhancement (i.e. UDF data enhancement) , to a certain extent, to solve the problem of the scarcity of medical data samples. Based on augmented data, a medical event extraction model (i.e. TEC_MEE model) based on Transformer Encoder and CRF are constructed to extract attributes of specified events from unstructured Chinese electronic medical record text. The experimental results show that, compared with the baseline model, the TEC_MEE model proposed in this paper obtains better medical event extraction results after data enhancement.

show abstract

Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection

Liao,

Wang,

Zhan

et al. 2023

Phys Eng Sci Med

View full text Add to dashboard Cite

Automatic detection of epileptic seizure events using the time-frequency features and machine learning

Cited by 17 publications

References 44 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

Small sample medical event extraction based on data augmentation

Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection

Contact Info

Product

Resources

About