Automatic Seizure Detection using Inter Quartile Range

Bedeeuzzaman, M.; Farooq, Omar; Khan, Yusuf Uzzaman

doi:10.5120/6304-8614

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…In accordance with previous research [ 66 ], seizure epochs display higher values of both the Interquartile Range and Standard Deviation as compared to non-seizure epochs. Entropy, however, shows similar values across seizure and non-seizure epochs.…”

Section: Resultssupporting

confidence: 92%

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Al-Hussaini

Mitchell

2023

Bioengineering

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This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

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Section: Resultssupporting

confidence: 92%

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Al-Hussaini

Mitchell

2023

Bioengineering

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“…This study utilizes a subset of EEG data obtained from the University of Bonn, Germany 26–28 . The complete dataset is comprised of five sets (A–E), each containing 100 single‐channel EEG segments.…”

Section: Proposed Methodsmentioning

confidence: 99%

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

Alam,

Hasan,

Farooq

et al. 2023

Engineering Reports

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Epilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

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“…Thus, regardless of the length of the signal, the five features would be generated. The mathematical equations of the five statistical characteristics are as shown in Equations ( 4)- (8).…”

Section: First Order Statisticmentioning

confidence: 99%

“…for feature extraction and combine with machine learning in the classification stage. A time-domain approach to EEG epileptic classification was reported in [6]- [8]. In those studies, first-order statistical calculations were used for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

2021

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Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

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Automatic Seizure Detection using Inter Quartile Range

Cited by 11 publications

References 21 publications

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

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