Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure

Bandarabadi, Mojtaba; Rasekhi, Jalil; Teixeira, César; Netoff, Theoden I.; Parhi, Keshab K.; Dourado, António

doi:10.1142/s0129065715500197

Cited by 26 publications

(11 citation statements)

References 82 publications

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“…Nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain [43,44,45,46,47,48,49]. They help in the identification of underlying behavior of biological signals [11,50,51], such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all non-stationary signals.…”

Section: Analysis Of Sleep Eeg Signals Using Nonlinear Dynamics Mementioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Analysis Of Sleep Eeg Signals Using Nonlinear Dynamics Mementioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…Finally, we emphase at this point that the sensitivity and specific values reported above are for the specific test dataset [54], which is limited in many ways. To reliably determine the sensitivity and specificity of the method in a clinical setting it is necessary to conduct a thorough evaluation by using longterm and continuous EEG signals (see for example the protocols adopted in [55]). A thorough evaluation of the performance of the proposed methods is a main perspective for future work.…”

Section: Resultsmentioning

confidence: 99%

Fast statistical model-based classification of epileptic EEG signals

Quintero-Rincón

Pereyra

D’Giano

et al. 2018

Biocybernetics and Biomedical Engineering

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This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

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“…The performance can be improved by inserting sensitivity, robustness objectives and constraints (Casavola et al, 2014;Gutiérrez-Carvajal et al, 2016). The pole placement method applied in this paper can be replaced by the optimal design and tuning by means of classical or modern optimization algorithms (Bandarabadi et al, 2015;Johanyák, 2015;Menchaca-Mendez and Coello Coello, 2016). This has not been investigated, but it represents a subject of future research.…”

Section: Discussionmentioning

confidence: 99%

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

Szedlak-Stînean

Precup

Preitl

et al. 2016

Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics

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This paper presents details regarding the design of two state feedback control (SFC) solutions for the position control of a mechatronics application represented by the Model 220 Industrial Plant Emulator. Since SFC is not effective in terms of zero steady-state control error, the SFC structure of both solutions is inserted in a control loop that contains a PID controller with or without low-pass filter. This leads to the two SFC solutions proposed in this paper and dedicated to mechatronics applications with variable moment of inertia. The PID controllers are tuned by the Modulus Optimum method to ensure high control system performance expressed as increased phase margins and improved tracking performance. The performance of the proposed SFC solutions is illustrated by case studies that deal with experimentally identified parameters in two situations, rigid body dynamics and flexible drive dynamics. Simulation and experimental results obtained for the three significant values of the moment of inertia of the load disk are given.

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Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure

Cited by 26 publications

References 82 publications

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Fast statistical model-based classification of epileptic EEG signals

State Feedback Control Solutions for a Mechatronics System with Variable Moment of Inertia

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