4,202 research outputs found
Performance metrics for characterization of a seizure detection algorithm for offline and online use
Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics. Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s. Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately. Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decrease in performance may be expected when an offline seizure detection algorithm (especially with short epoch duration) is used for online analysis.Accepted versio
Dynamical Component Analysis (DyCA) and its application on epileptic EEG
Dynamical Component Analysis (DyCA) is a recently-proposed method to detect
projection vectors to reduce the dimensionality of multi-variate deterministic
datasets. It is based on the solution of a generalized eigenvalue problem and
therefore straight forward to implement. DyCA is introduced and applied to EEG
data of epileptic seizures. The obtained eigenvectors are used to project the
signal and the corresponding trajectories in phase space are compared with PCA
and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection
and the obtained results in terms of specificity, false discovery rate and miss
rate are compared to other seizure detection algorithms.Comment: 5 pages, 4 figures, accepted for IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP) 201
Neonatal Seizure Detection using Convolutional Neural Networks
This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin
- …