Measuring time series regularity using nonlinear similarity-based sample entropy

Xie, Huimin; He, Weijun; Liu, Hui

doi:10.1016/j.physleta.2008.10.049

Cited by 100 publications

(61 citation statements)

References 11 publications

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“…The second approach is based on computing the sample entropy (SampEn) value of the signal. SampEn is an improvement of approximate entropy (ApEn) [11], which is a method for quantifying the amount of regularity in data [12,19]. SampEn is more consistent with previous research results compared to the ApEn approach [11].…”

Section: Evaluation Approaches For Quantifying Filtered Signal Efficisupporting

confidence: 77%

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

Entropy

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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.

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Section: Evaluation Approaches For Quantifying Filtered Signal Efficisupporting

confidence: 77%

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

Entropy

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“…Feature extraction and classification of brain signals specifically depends on the neurological phenomenon to be analyzed, nature of episodes, characteristics relationships of source signals on applications, similarity matrices and correlations, entropy and other factors [92]. Classical patter classification algorithms can be applied for many types of applications [93].…”

Section: Processing Of Brain Signalsmentioning

confidence: 99%

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

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“…The sample entropy is the most common entropy formulation to be used for analyzing physiological signals (Costa et al, 2005b). A useful variation to the original multiscale entropy algorithm uses the modified sample entropy defined in (Xie et al, 2008). The practical effect of using the modified sample entropy is the computed entropy values are more robust to noise and results are more consistent with short time series.…”

Section: Univariate Measuresmentioning

confidence: 99%

“…The practical effect of using the modified sample entropy is the computed entropy values are more robust to noise and results are more consistent with short time series. In brief, the similarity functions A m and B m defined by equations (7) and (9) in (Xie et al, 2008) are computed for each coarse-grained time series defined in equation 1. The modified multiscale entropy (mMSE) is then defined as the series of modified sample entropy values at each of the coarse grain scales.…”

Section: Univariate Measuresmentioning

confidence: 99%

Neurotechnology and Psychiatric Biomarkers

Bosl¹

2012

Biomedical Science, Engineering and Technology

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Measuring time series regularity using nonlinear similarity-based sample entropy

Cited by 100 publications

References 11 publications

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

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

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Neurotechnology and Psychiatric Biomarkers

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