Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features

Pattnaik, Snigdha; Rout, Nirmal Kumar; Sabut, Sukanta

doi:10.1007/s41870-022-00877-1

Cited by 19 publications

(12 citation statements)

References 35 publications

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“…The dataset consisted of text files that were classified in binary as 0 or 1, meaning non-seizure and epileptic seizure, respectively. With this data, two matrices were generated containing the sampled signal data Dataset from Children’s Hospital called CHB-MIT EEG [ 4 , 9 , 30 , 36 , 38 ]. They recorded 22 patients at various times and made 654 files with uncontrollable seizures that cannot be controlled by medicines.…”

Section: Discussionmentioning

confidence: 99%

“…A wide number of seizure detection approaches have been developed by using multiple machine learning classifiers as well as features. The selection of an appropriate classifier and feature is the major challenge [ 9 ]. Therefore, this study provides an analysis of the feature-extracting methodologies used for the detection of epileptic seizures along with the ML techniques.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

2023

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Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

2023

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“…Several studies have implemented advanced timefrequency methods for analyzing and processing biopotential signals, such as EMG and EEG. For example, the tunable Q-wavelet transform (TQWT), combined with timefrequency features, was used to detect epileptic seizures using the EEG signal [190]. A recent study used the TQWT method to diferentiate seven hand movements using the surface-EMG signal [191].…”

Section: Limitations Challenges and Suggestions For Future Researchmentioning

confidence: 99%

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

Pradhan

Neelappu

Jayaraman

et al. 2023

Journal of Healthcare Engineering

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The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

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“…In addition, 1D CNN models are essentially appropriate to process EEG time-series data. In [20], the wavelet transform can be utilized to detect and classify epileptic seizures on EEG signals and classified with ML models such as SVM and RF.…”

Section: Literature Reviewmentioning

confidence: 99%

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

et al. 2022

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Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features

Cited by 19 publications

References 35 publications

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

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