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Micro analysis of spike and wave bursts in children EEG
Sl13 PI 1.03 MICRO ANALYSIS OF SPIKE AND WAVE BURSTS IN CHILDREN'S EEG.
lead, agreement was only 81%. Thus, the proof of the programme system on a 'test set' confirmed the results obtained by the 'learning set' (91%).
Aria M. Tomb, J.C. Principe and A. Martins da Silva (Aveiro, Portugal) An automated system to detect and characterize spike and wave bursts with high accuracy was utilized to measure some parameters of the spike-and-wave complexes, like spike and slow wave amplitude, spike and slow wave duration, repetition rate of the complexes and duration of the paroxysms. A group of seven children (aged 3-12 years, 1 male, 6 females) were monitored twice during 3 hours, and the EEG recorded in a 7 channel FM tape recorder. One of the frontal channels (F3-C3 or F4-C4) was analyzed with a real time programme implemented in a microcomputer. The children came to the outpatient clinic of the Hospital Santo Antonio, Porto, Portugal, without anti-convulsant medication and their EEG was recorded immediately. The second recording was done 5 months after starting medication with valproic acid (3 patients) or Suximal (4 patients). This work will present data regarding the waveform characteristics of each paroxysm, automatically measured by the system. Comparisons between the parameters before and after drug administration are also presented.
PII.04 EXPERIENCES WITH A M E T H O D DESIGNED FOR R E C O G N I T I O N OF EEG S Y N C H R O N I S A T I O N AND DESYNCHRONISATION.
72 Jardanhitzv, Z. Ori and G. Marosi (Szeged, Hungary) Based on the comparison of several methods of quantitative an alysis, an experimental programme system was constructed and tested during surgery. Because of the relative complexity and slowness of spectral analysis by an autoregressive model-fitting programme, the best parameters of the other two analyses were chosen instead. Their reliability was enhanced by combination of both methods. After calculating the appropriate parameters, the values were substituted in discriminating equations gained during a previous analysis of desynchronizing and synchronizing reactions in light thiobarbiturate narcosis. The test was performed 'off-line' by comparison of the results obtained by visual and automatic scoring of EEG reactions to electrical stimulation during ketamine narcosis. In spite of the fact that these synchronizing and desynchronizing reactions differed from those encountered during barbiturate anaesthesia, since here the changes in frequency and amplitude were not closely correlated, it was possible to get reliable results. There were some differences in results obtained by analysis of different leads. In the right fronto-parietal lead, an 86% agreement with visual analysis was found. In the biparietal
Pll.05 QUANTIFICATION OF IRREGULAR ACTIVITY IN CLINICAL EEG WITH RHYTHM FACTOR.
K. Takahashi and K. Kita (Chiba and Tochigi, Japan) We have proposed a method of quantifying irregular activity by calculating 'Rhythm Factor' at the 10th ICMBE (1973), which showed the wave-to-wave variability of the amplitude and frequency of the spontaneous EEG waves. The Rhythm Factor is defined by the following equation: n
Rhythm Factor = 1 / n E
F(3 - 120b)gP, ~ ~/Pi I)
i--1
where F ( x ) = l / 2 ( x / t x l +1 with F ( x ) = l for x = 0 . and Pi indicating the required data series. This means that a series of values of amplitude a n d / o r frequency can be transformed into successive numeric values ([0] or [1]) by taking the consecutive ratio of two adjacent data and being converted to decibel (dB) value. The detection of EEG waves is based on a method of approximating to the visual assessment like that of Remond's mimetic analysis. The basic segment of analysing epochs have a duration of 2 seconds with I second overlap in time. The material comprises six types of wakening EEG records which vary from normal to severely abnormal. The comparison with visual evaluation showed a good correspondence with the degree of irregularity at the level of 70 and 30% with the Rhythm Factor. The comparison with the variance of power spectrum revealed a poor correlation of irregularity with background activity.
P I I . 0 6 S H O R T - T E R M VARIABILITY FREQUENCY ANALYSIS.
IN
EEG
AND
B.S. Oken and K.H. Chiappa (Boston, MA, USA) Knowledge of short-term EEG variability in computerized analysis is important because changes may be due to inherent variability and may not necessarily be attributed to task (e.g. listening to a story), therapy or changes in underlying disease, as has been frequently reported. Eighty to 120 seconds of 16-channel, artifact-free bipolar EEGs were recorded in normal subjects and analyzed using an FFT. Absolute and relative power in 5 standard EEG frequency bands and median and peak power frequencies were obtained for each 4-second epoch, and the mean and standard deviation were calculated for each parameter. The same 20-30 epochs of EEG were analyzed two different ways: the first vs. the second half and the odd vs. the
lead, agreement was only 81%. Thus, the proof of the programme system on a 'test set' confirmed the results obtained by the 'learning set' (91%).
Aria M. Tomb, J.C. Principe and A. Martins da Silva (Aveiro, Portugal) An automated system to detect and characterize spike and wave bursts with high accuracy was utilized to measure some parameters of the spike-and-wave complexes, like spike and slow wave amplitude, spike and slow wave duration, repetition rate of the complexes and duration of the paroxysms. A group of seven children (aged 3-12 years, 1 male, 6 females) were monitored twice during 3 hours, and the EEG recorded in a 7 channel FM tape recorder. One of the frontal channels (F3-C3 or F4-C4) was analyzed with a real time programme implemented in a microcomputer. The children came to the outpatient clinic of the Hospital Santo Antonio, Porto, Portugal, without anti-convulsant medication and their EEG was recorded immediately. The second recording was done 5 months after starting medication with valproic acid (3 patients) or Suximal (4 patients). This work will present data regarding the waveform characteristics of each paroxysm, automatically measured by the system. Comparisons between the parameters before and after drug administration are also presented.
PII.04 EXPERIENCES WITH A M E T H O D DESIGNED FOR R E C O G N I T I O N OF EEG S Y N C H R O N I S A T I O N AND DESYNCHRONISATION.
72 Jardanhitzv, Z. Ori and G. Marosi (Szeged, Hungary) Based on the comparison of several methods of quantitative an alysis, an experimental programme system was constructed and tested during surgery. Because of the relative complexity and slowness of spectral analysis by an autoregressive model-fitting programme, the best parameters of the other two analyses were chosen instead. Their reliability was enhanced by combination of both methods. After calculating the appropriate parameters, the values were substituted in discriminating equations gained during a previous analysis of desynchronizing and synchronizing reactions in light thiobarbiturate narcosis. The test was performed 'off-line' by comparison of the results obtained by visual and automatic scoring of EEG reactions to electrical stimulation during ketamine narcosis. In spite of the fact that these synchronizing and desynchronizing reactions differed from those encountered during barbiturate anaesthesia, since here the changes in frequency and amplitude were not closely correlated, it was possible to get reliable results. There were some differences in results obtained by analysis of different leads. In the right fronto-parietal lead, an 86% agreement with visual analysis was found. In the biparietal
Pll.05 QUANTIFICATION OF IRREGULAR ACTIVITY IN CLINICAL EEG WITH RHYTHM FACTOR.
K. Takahashi and K. Kita (Chiba and Tochigi, Japan) We have proposed a method of quantifying irregular activity by calculating 'Rhythm Factor' at the 10th ICMBE (1973), which showed the wave-to-wave variability of the amplitude and frequency of the spontaneous EEG waves. The Rhythm Factor is defined by the following equation: n
Rhythm Factor = 1 / n E
F(3 - 120b)gP, ~ ~/Pi I)
i--1
where F ( x ) = l / 2 ( x / t x l +1 with F ( x ) = l for x = 0 . and Pi indicating the required data series. This means that a series of values of amplitude a n d / o r frequency can be transformed into successive numeric values ([0] or [1]) by taking the consecutive ratio of two adjacent data and being converted to decibel (dB) value. The detection of EEG waves is based on a method of approximating to the visual assessment like that of Remond's mimetic analysis. The basic segment of analysing epochs have a duration of 2 seconds with I second overlap in time. The material comprises six types of wakening EEG records which vary from normal to severely abnormal. The comparison with visual evaluation showed a good correspondence with the degree of irregularity at the level of 70 and 30% with the Rhythm Factor. The comparison with the variance of power spectrum revealed a poor correlation of irregularity with background activity.
P I I . 0 6 S H O R T - T E R M VARIABILITY FREQUENCY ANALYSIS.
IN
EEG
AND
B.S. Oken and K.H. Chiappa (Boston, MA, USA) Knowledge of short-term EEG variability in computerized analysis is important because changes may be due to inherent variability and may not necessarily be attributed to task (e.g. listening to a story), therapy or changes in underlying disease, as has been frequently reported. Eighty to 120 seconds of 16-channel, artifact-free bipolar EEGs were recorded in normal subjects and analyzed using an FFT. Absolute and relative power in 5 standard EEG frequency bands and median and peak power frequencies were obtained for each 4-second epoch, and the mean and standard deviation were calculated for each parameter. The same 20-30 epochs of EEG were analyzed two different ways: the first vs. the second half and the odd vs. the