Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

Niknazar, Hamid; Maghooli, Keivan; Nasrabadi, Ali Motie

doi:10.5120/19799-1578

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…The signal was pre-processed by applying notch filters at 50 and 100 Hz to mitigate power line interference [36], a high-pass filter at 1 Hz to remove DC offset and baseline fluctuations [37,38], and a low-pass filter at 125 Hz to maintain higher frequencies that could characterize abnormal brain activity [39,40]. Both datasets were also downsampled to 128 Hz to reduce the computational cost of model training [41,42].…”

Section: Eeg Signal Pre-processingmentioning

confidence: 99%

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

Shafiezadeh,

Duma,

Mento

et al. 2024

Sensors

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The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

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Section: Eeg Signal Pre-processingmentioning

confidence: 99%

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

Shafiezadeh,

Duma,

Mento

et al. 2024

Sensors

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“…The method of iteratively selecting the optimal features for each patient still suffers from the problem of great time complexity. Therefore, in the case of ensuring acceptable performance, the 11 summarized features (line length-beta [12], local extrema [13], Higuchi FD-beta, zero-crossings, line length-alpha, line length-theta, Higuchi FD [14], SVDEn [15], peak frequency [16], Hurst exponent [15,17], and Higuchi FD-alpha) that are commonly applied in seizure prediction can still be used as a guide for general features of all patients.…”

Section: Comparison Of Different Feature Design Principlesmentioning

confidence: 99%

Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time–Frequency Domain Measures

Zheng

Peng

2021

Processes

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The prediction of epileptic seizures is crucial to aid patients in gaining early warning and taking effective intervention. Several features have been explored to predict the onset via electroencephalography signals, which are typically non-stationary, dynamic, and varying from person-to-person. In the former literature, features applied in the classification have shared similar contributions to all patients. Therefore, in this paper, we analyze the impact of the specific combination of feature and channel from time, frequency, and time–frequency domains on prediction performance of disparate patients. Based on the minimal-redundancy-maximal-relevance criterion, the proposed framework uses a sequential forward selection approach to individually find the optimal features and channels. Trained models could discriminate the pre-ictal and inter-ictal electroencephalography with a sensitivity of 90.2% and a false prediction rate of 0.096/h. We also present the comparison between the classification accuracy obtained by the optimal features, several features summarized from optimal features, and the complete set of features from three domains. The results indicate that various patient interpretations have a certain specificity in the selection of feature-channel. Furthermore, the detailed list of optimal features and summarized features are proffered for reference to those who research the corresponding database.

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“…The key element of such analysis is to identify the suitable multiplier, M, to apply. From the vector that contained the sequence of symbols, V s , it is possible to evaluate several statistical features [24,45]. However, to avoid redundant information (taking into consideration that frequency-based features and features focused on the relation between amplitude and frequency were employed in the other two categories), the vector analysis was based on the number of occurrences of each symbol.…”

Section: Feature Creationmentioning

confidence: 99%

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2021

J. Neural Eng.

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The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

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Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

Cited by 12 publications

References 21 publications

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time–Frequency Domain Measures

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

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