The Brain function index as a depth of anesthesia indicator using complexity measures

Shalbaf, Reza; Behnam, Hamid; Moghadam, H. Jelveh; Mehrnam, A.H.; Sadaghiani, Mohammad Hossein

doi:10.1109/spc.2013.6735105

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…BIS was recorded at a frequency of 1.0 Hz. In this database, the age range of patients varied from 15 and 70 years [16].…”

Section: Experimental Datasetmentioning

confidence: 99%

“…After administering a hypnotic drug, the patient enters a sedation state and EEG shows increased beta activity (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) [4]. After the sedation state, four stages occur: phase 1 is a light anesthetic state with decreased EEG beta activity (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and increased alpha (8)(9)(10)(11)(12) and delta activities (0-4 Hz) [5,6]. In phase 2, an intermediate state, beta activity decreases and alpha and delta activities increase [7].…”

Section: Introductionmentioning

confidence: 99%

“…Anesthetic states based on BIS[14]. Hz), mid beta(15)(16)(17)(18), high beta (22-35 Hz), gamma (35-70 Hz)) were evaluated at different levels of anesthesia, and in the second approach, due to regular EEG activity waves during anesthesia, features such as approximate entropy (ApEn), permutation entropy (PE), Shannon entropy (SE), detrended fluctuation analysis (DFA), and fractal dimension (FD) were investigated. In total, q = 12 features were extracted from EEG signals for complexity measures.…”

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confidence: 99%

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Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Jahanseir¹,

Setarehdan²,

Momenzadeh³

2018

Turk J Elec Eng & Comp Sci

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Today, most surgeries are performed under general anesthesia where one of the most growing methods for anesthesia depth monitoring is using electroencephalogram (EEG). The bispectral index (BIS) is the most commonly used parameter for anesthesia depth monitoring using EEG, the validity of which is still to be studied before being accepted as a routine method by clinicians. This paper proposes a new technique for detecting the depth of anesthesia by means of EEG, which is based on multioutput least-squares support vector regression (MLS-SVR), which provides the probability that the patient is in the four different possible anesthesia states. In this study, EEG signals were recorded from 20 patients who were anesthetized in the operation room. Twelve linear and nonlinear EEG features were then extracted every 10 s from the EEG signals to form the feature vector. The features were then classified by the MLS-SVR classifier and the results were compared with those of the BIS, where no significant differences were observed (P > 0.05). Due to using the MLS-SVR classifier, which replaces quadratic equations by linear equations, the proposed method shows a higher accuracy compared to the other previously reported methods.

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“…BIS was recorded at a frequency of 1.0 Hz. In this database, the age range of patients varied from 15 and 70 years [16].…”

Section: Experimental Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Jahanseir¹,

Setarehdan²,

Momenzadeh³

2018

Turk J Elec Eng & Comp Sci

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Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

2014

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Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

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Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

Jahanseir

Setarehdan

Momenzadeh

2018

Australas Phys Eng Sci Med

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The Brain function index as a depth of anesthesia indicator using complexity measures

Cited by 4 publications

References 19 publications

Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

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