A comparison of different classification algorithms for determining the depth of anesthesia level on a new set of attributes

This paper considers one of the challenging tasks during surgical procedure, i.e. depth of anasthesia estimate. The purpose of this paper is to investigate the effect of the analyzed EEG signal fragment duration on the accuracy of anesthesia level estimate using the linear discriminant analysis algorithm and determining the EEG signal length, which yields acceptable accuracy of anesthesia level separation using these parameters.A new method for classifying EEG anesthesia levels is proposed. The possibility of classifying levels of anesthesia is demonstrated by means of sharing the EEG parameters under consideration (SE, BSR, SEF95, RBR). The method can be used in anesthesia monitors that are used to monitor the depth of anesthesia in order to select the appropriate dose of anesthetic drugs during operations, thus avoiding both cases of intraoperative arousal and excessively deep anesthesia.

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A Wavelet Transform Based Method to Determine Depth of Anesthesia to Prevent Awareness during General Anesthesia

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/354739 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Seyed Mortaza Mousavi ◽

Ahmet Adamoğlu ◽

Tamer Demiralp ◽

Mahrokh G. Shayesteh

Keyword(s):

Wavelet Transform ◽

General Anesthesia ◽

Depth Of Anesthesia ◽

Complex Wavelet Transform ◽

Eeg Signals ◽

Phase Coupling ◽

Important Challenge ◽

Eeg Changes ◽

Anesthesia Level ◽

Electroencephalogram Eeg

Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. We analyzed EEG changes for phase coupling between delta and alpha subbands using a new algorithm for depth of general anesthesia measurement based on complex wavelet transform (CWT) in patients anesthetized by Propofol. Entropy and histogram of modulated signals were calculated by taking bispectral index (BIS) values as reference. Entropies corresponding to different BIS intervals using Mann-WhitneyUtest showed that they had different continuous distributions. The results demonstrated that there is a phase coupling between 3 and 4 Hz in delta and 8-9 Hz in alpha subbands and these changes are shown better at the channelT7of EEG. Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented.

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Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

Journal of Psychophysiology ◽

10.1027//0269-8803.14.3.151 ◽

2000 ◽

Vol 14 (3) ◽

pp. 151-158 ◽

Cited By ~ 6

Author(s):

José Luis Cantero ◽

Mercedes Atienza

Keyword(s):

Rem Sleep ◽

Spectral Component ◽

Sleep Onset ◽

Quantitative Information ◽

Maximum Energy ◽

Alpha Activity ◽

The Other ◽

Classification Algorithms ◽

Microstructural Properties ◽

Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

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