Jean Gotman

Automatic recognition of epileptic seizures in the EEG

During prolonged EEG monitoring of epileptic patients, the continuous EEG tracing may be replaced by a selective recording of ictal and interictal epileptic activity. We have described previously methods for the EEG recording of seizures with overt clinical manifestations and for the automatic detection of spikes. This paper describes a method for the automatic detection of seizures in the EEG, independently of the presence of clinical signs; it is based on the decomposition of the EEG into elementary waves and the detection of paroxysmal bursts of rhythmic activity having a frequency between 3 and 20 c/sec. Simple procedures are used to measure the amplitude of waves relative to the background, their duration and rhythmicity. The evaluation of the method on 24 surface recordings (average duration 12.4 h) and 44 recordings from intracerebral electrodes (average duration 18.7 h) indicated that it was capable of recognizing numerous types of seizures. False detections due to non-epileptiform rhythmic EEG bursts and to artefacts were quite frequent but were not a serious problem because they did not unduly lengthen the EEG tracing and they could be easily identified by the electroencephalographer. The program can perform on-line and simultaneously the automatic recognition of spikes and of seizures in 16 channels.

Interictal high‐frequency oscillations (80–500 Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain

Purpose:  High‐frequency oscillations (HFOs) known as ripples (80–250 Hz) and fast ripples (250–500 Hz) can be recorded from macroelectrodes inserted in patients with intractable focal epilepsy. They are most likely linked to epileptogenesis and have been found in the seizure onset zone (SOZ) of human ictal and interictal recordings. HFOs occur frequently at the time of interictal spikes, but were also found independently. This study analyses the relationship between spikes and HFOs and the occurrence of HFOs in nonspiking channels.

High-Frequency Electroencephalographic Oscillations Correlate With Outcome of Epilepsy Surgery

High‐frequency oscillations (HFOs) in the intracerebral electroencephalogram (EEG) have been linked to the seizure onset zone (SOZ). We investigated whether HFOs can delineate epileptogenic areas even outside the SOZ by correlating the resection of HFO‐generating areas with surgical outcome.

Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG

An attempt was made at using a small computer to recognize and quantify interictal epileptic activity (spikes and sharp waves) in the human scalp EEG. To perform the automatic recognition, the EEG of each channel is broken down into half-waves. A half-wave is characterized by its duration and its amplitude relative to the background activity. A wave is characterized by the durations and amplitudes of its two component half-waves, by the second derivative at its apex measured relative to the background activity, and by the duration and amplitude of the following half-wave. Particular combinations of these parameters were found to characterize spikes and sharp waves and are used for their recognition and quantification. Specific methods are used for the rejection of spike-like or sharp wave-like wave forms such as eye blinks, muscle potentials and sharp alpha activity and were found to perform with a high level of reliability. Interchannel relationships are thoroughly examined to determine areas of maximal epileptogenicity. Sixteen channels can be analyzed in real time. Results are presented in a simple picture containing localizing and quantitative information. Specific questions regarding the time relationships of spikes in different channels can be asked interactively by the user. The system is of potential use in clinical electroencephalography.

Interictal spiking during wakefulness and sleep and the localization of foci in temporal lobe epilepsy.

We examined variations in interictal spiking during sleep and wakefulness to assess differences in reliability for localizing epileptic foci. Forty patients were studied prospectively. Spikes were assessed for rates, field, and appearance of new foci. Final localization was determined by surgery, electrocorticography, and seizure onset. Comparison of interictal EEG foci with final localization was made. In 39 patients, slow-wave sleep activated spiking compared with wakefulness. Most patients showed maximal spiking in sleep stages 3 or 4. Restriction of field in rapid eye movement (REM) sleep and wakefulness, and extension of field in slow-wave sleep occurred. New foci appeared in non-rapid eye movement sleep in 53% of patients. Similar but not identical spiking rates, foci, and field distributions were seen in wakefulness and REM sleep. All REM foci were unilateral. Our findings suggest that localization of the primary epileptogenic area is more reliable in REM sleep than in wakefulness, and in wakefulness more than in slow-wave sleep.

Automatic seizure detection: improvements and evaluation ☆

Improvements to an existing automatic seizure detection program are described. They are aimed at taking into account a larger temporal context and thus improving the specificity of the detections. Results were evaluated on 293 recordings from 49 patients, totaling 5303 h of 16-channel recording. They showed that 24% of the 244 seizures recorded were missed by the automatic detection; in 41% of the seizures, the patient alarm was not pressed but the computer made detections. The false detection rate was of the order of 1 false detection per hour of recording. Conclusions are: (1) automatic seizure detection must be used in conjunction with a patient alarm button since some seizures, having poorly defined EEG activity, are not detected; (2) the automatic detection allowed capture of many seizures, clinical and subclinical, for which the alarm was not pressed; (3) the low false detection rate indicates that lower detection threshold could be used, yielding better seizure detection.

A system to detect the onset of epileptic seizures in scalp EEG.

OBJECTIVE A new method for automatic seizure detection and onset warning is proposed. The system is based on determining the seizure probability of a section of EEG. Operation features a user-tuneable threshold to exploit the trade-off between sensitivity and detection delay and an acceptable false detection rate. METHODS The system was designed using 652 h of scalp EEG, including 126 seizures in 28 patients. Wavelet decomposition, feature extraction and data segmentation were employed to compute the a priori probabilities required for the Bayesian formulation used in training, testing and operation. RESULTS Results based on the analysis of separate testing data (360 h of scalp EEG, including 69 seizures in 16 patients) initially show a sensitivity of 77.9%, a false detection rate of 0.86/h and a median detection delay of 9.8 s. Results after use of the tuning mechanism show a sensitivity of 76.0%, a false detection rate of 0.34/h and a median detection delay of 10 s. Missed seizures are characterized mainly by subtle or focal activity, mixed frequencies, short duration or some combination of these traits. False detections are mainly caused by short bursts of rhythmic activity, rapid eye blinking and EMG artifact caused by chewing. Evaluation of the traditional seizure detection method of using both data sets shows a sensitivity of 50.1%, a false detection rate of 0.5/h and a median detection delay of 14.3 s. CONCLUSIONS The system performed well enough to be considered for use within a clinical setting. In patients having an unacceptable level of false detection, the tuning mechanism provided an important reduction in false detections with minimal loss of detection sensitivity and detection delay. SIGNIFICANCE During prolonged EEG monitoring of epileptic patients, the continuous recording may be marked where seizures are likely to have taken place. Several methods of automatic seizure detection exist, but few can operate as an on-line seizure alert system. We propose a seizure detection system that can alert medical staff to the onset of a seizure and hence improve clinical diagnosis.

Wavelet based automatic seizure detection in intracerebral electroencephalogram

BACKGROUND Automatic seizure detection is often used during long-term monitoring, and is particularly important during intracerebral investigations. Existing methods make many false detections, particularly in intracerebral electroencephalogram (EEG) because of frequent large amplitude rhythmic activity bursts that are non-epileptiform. OBJECTIVE To develop a seizure detection method for intracerebral monitoring that is as sensitive as existing methods but has fewer false detections. METHODS To capture the rhythmic nature of seizure discharges, we developed a wavelet-based method, examining how different frequency ranges fluctuate compared to the background. In particular, the system remembers rhythmic bursts occurring commonly in the background to avoid detecting them as seizures. RESULTS The method was evaluated on test data from 11 patients, including 229 h and 66 seizures, and its performance compared to the method of Gotman (Electroencephalogr clin Neurophysiol 76 (1990) 317). Detection sensitivity was unchanged at close to 90%, but false detections were reduced from 2.4 to 0.3/h. CONCLUSIONS Perfect sensitivity is unlikely because the morphology of seizure discharges is so variable. Nevertheless, the 87% sensitivity obtained in the combined training and testing data is quite high. We reduced the average false alarm rate to one per 3 h of recording, or 6 per 24-h period. Given how rapidly one can decide visually that a detection is erroneous, false detections should not cause any burden to the reviewer. SIGNIFICANCE In intracerebral EEG it is possible to detect seizures automatically with high sensitivity and high specificity.

A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device

During long-term electroencephalogram (EEG) monitoring of epileptic patients, a seizure warning system would allow patients and observers to take appropriate precautions. It would also allow observers to interact with patients early during the seizure, thus revealing clinically useful information. We designed patient-specific classifiers to detect seizure onsets. After a seizure and some nonseizure data are recorded in a patient, they are used to train a classifier. In subsequent monitoring sessions, EEG patterns have to pass this classifier to determine if a seizure onset occurs. If it does, an alarm is triggered. Extreme care has been taken to ensure a low false-alarm rate, since a high false-alarm rate would render the system ineffective. Features were extracted from the time and frequency domains and a modified nearest-neighbor (NN) classifier was used. The system reached an onset detection rate of 100% with an average delay of 9.35 s after onset. The average false-alarm rate was only 0.02/h. The method was evaluated in 12 patients with a total of 47 seizures. Results indicate that the system is effective and reasonably reliable. Computation load has been kept to a minimum so that real-time processing is possible.

Heart Rate Changes and ECG Abnormalities During Epileptic Seizures: Prevalence and Definition of an Objective Clinical Sign

Summary:  Purpose: To determine the prevalence of heart rate changes and ECG abnormalities during epileptic seizures and to determine the timing of heart rate changes compared to the first electrographic and clinical signs. To assess the risk factors for the occurrence of ECG abnormalities.