Detection of neonatal seizures through computerized EEG analysis

Liu, A; Hahn, Jin S.; Heldt, Gregory P.; Coen, Ronald W.

doi:10.1016/0013-4694(92)90179-l

Cited by 207 publications

(115 citation statements)

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“…Features were extracted in time domain [13], frequency domain [14][15][16], both time and frequency domain [17] or time-frequency domain like wavelet transform. Since EEG is nonstationary, the methods using time-frequency domain usually provide higher success than the other two methods.…”

Section: State Of the Artmentioning

confidence: 99%

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

Ayoubian¹,

Lacoma²,

Gotman³

2013

Medical Engineering & Physics

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Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures.

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Section: State Of the Artmentioning

confidence: 99%

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

Ayoubian¹,

Lacoma²,

Gotman³

2013

Medical Engineering & Physics

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“…To our best knowledge, the algorithms proposed by Liu et al [8], Gotman et al [5], and Celka and Colditz [1] are cited most frequently in this research field. In this chapter, we provide the reviews of the detection techniques used in these well known algorithms.…”

Section: Chapter 3 Neonatal Eeg Seizure Detectionmentioning

confidence: 99%

“…Based on the rhythmicity of the neonatal EEG seizures, Liu et at. perform analysis on the autocorrelation function of the EEG signals in [8].…”

Section: Motivationmentioning

confidence: 99%

“…The normal EEG signal with irregular peaks in its autocorrelation function should obtain low score. The criteria proposed in [8] is used to determine the existences of seizure activities in a 12-channel EEG signal. Since we are only interested in single-channel EEG, we modify the criteria to be: if one of the following is satisfied, the epoch contains seizures.…”

Section: Liu's Algorithmmentioning

confidence: 99%

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Neonatal Seizure Detection Using Blind Adaptive Fusion

Li¹

2011

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Seizure is the result of excessive electrical discharges of neurons, which usually develops synchronously and happens suddenly in the central nervous system. Clinically, it is difficult for physician to identify neonatal seizures visually, while EEG seizures can be recognized by the trained experts. Usually, in NICUs, EEG monitoring systems are used instead of the expensive on-site supervision. However, it is time-consuming to review an overnight recording, which motivates the researchers to develop automated seizure detection algorithms.Although, there are few detection algorithms existed in the literature, it is difficult to evaluate these mathematical model based algorithms since their performances vary significantly on different data sets. By extending our previous results on multichannel information fusion, we propose a distributed detection system consisting of the existing detectors and a fusion center to detect the seizure activities in the newborn EEG. The advantage of our technique is that it does not require any prior knowledge of the hypotheses or the detector performances, which are often unknown in real applications. Therefore, this proposed technique has the potential to improve the performances of the existing neonatal seizure detectors.In this thesis, we first review two newborn EEG models, one of which is used to generate neonatal EEG signals. The synthetic data is used later for testing purpose. IIIWe also review three existing algorithms and implement them to work as the local detectors. Then, we introduce the fusion algorithms applied in the fusion center for two different scenarios: large sample size and small sample size. We finally provide some numerical results to show the applicability, effectiveness, and the adaptability of the blind algorithms in the seizure detection problem. We also provide the testing results obtained using the synthetic to show the improvement of the detection system.

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“…4 Since long EEG is an important tool to evaluate infant with symptoms refer to central nervous system. 5 The present study was aimed to use EEG not only as a method of assessing neonatal seizures but also for formulating prognosis for long term developmental outcome.…”

Section: Introductionmentioning

confidence: 99%