Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

289

175

The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

Section: Introductionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

289

175

“…In this study, SampEn was used as a quantitative method to distinguish the different surgical stages, where it has been previously shown to be a practical and efficient method to monitor the DoA during surgeries in real time [5]. From the results in Table 2, we can see that there have been three cases that violate the clinical observation, in which SampEn at stage 1 are still lower than at stage 2 after filtering (for proposed and standard filtering approaches) is applied.…”

Section: Discussionmentioning

confidence: 99%

“…The original signal is decomposed repeatedly until its residue becomes a monotonic function. Hence, we can choose different suitably selected IMF combinations to reduce the noise in the signal and then re-construct the signal [5,16]. To illustrate this using the sample EEG data Figure 2 shows the decomposed IMFs of a 40 s segment of the EEG signals that were acquired.…”

Section: Proposed Signal Filtering Approach Used To Generate 3d Reprementioning

confidence: 99%

“…After the surgery ends, the patient gradually regains consciousness during the recovery stage and the EEG signals will tend to be more complex during this stage. In order to accurately evaluate the DoA from the EEG signal, instantaneous frequency provides a convenient way of decomposing the different frequencies of the EEG signal, which can help the anaesthetist to better understand the different signal characteristics associated with the degree of consciousness of a patient [5]. This can then be used to distinguish the different stages during a surgical procedure and as a consequence improve the monitoring of DoA though better managing the patients' responses to the amount of anaesthetic administered.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

Shih

Doctor

Fan

et al. 2015

Self Cite

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient's electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient's EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert-Huang transform (HHT). HHT OPEN ACCESSEntropy 2015, 17 929 uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

“…Recently, Bandt and Pompe proposed the Permutation Entropy (PE) method to measure the irregularity of non-stationary time series [23], where the basic idea is to consider order relations between the values of a time series rather than the values themselves. Compared with approximate entropy and sample entropy [24][25][26], the advantages of the PE method are its simplicity, low complexity in computation without further model assumptions, and robustness in the presence of observational and dynamical noise [23,27]. Cao et al used permutation entropy to identify various phases of epileptic activity in the intracranial EEG signals recorded from three patients suffering from intractable epilepsy [28].…”

Section: Introductionmentioning

confidence: 99%

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Yan

Liu

et al. 2014

Abstract:In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.