Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison

Labate, Domenico; Foresta, Fabio La; Occhiuto, Gianluigi; Morabito, Francesco Carlo; Lay-Ekuakille, A.; Vergallo, P.

doi:10.1109/jsen.2013.2257742

Cited by 175 publications

(61 citation statements)

References 27 publications

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“…Before performing MSE, we employed empirical mode decomposition (EMD) [19] to remove trend (last component of EMD) from the epoched EEG data. This detrend process by EMD has been demonstrated as efficient to improve the stationarity of the signals [20][21][22][23]. For stop trials, the MSE analysis was performed from scale 1 to 30 (the resolution of EEG signals is 1 ms) in two time windows: (1) −750~−300 ms; and (2) −300~150 ms relative to the stop signal onset.…”

Section: Mse Analysismentioning

confidence: 99%

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Huang

Tseng²,

Liang

2015

Entropy

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Abstract:The ability to inhibit impulses and withdraw certain responses are essential for human's survival in a fast-changing environment. These processes happen fast, in a complex manner, and require our brain to make a fast adaptation to inhibit the impulsive response. The present study employs multiscale entropy (MSE) to analyzing electroencephalography (EEG) signals acquired alongside a behavioral stop-signal task to theoretically quantify the complexity (indicating adaptability and efficiency) of neural systems to investigate the dynamical change of complexity in the brain during the processes of inhibitory control. We found that the complexity of EEG signals was higher for successful than unsuccessful inhibition in the stage of peri-stimulus, but not in the pre-stimulus time window. In addition, we found that the dynamical change in the brain from pre-stimulus to peri-stimulus stage for inhibitory control is a process of decreasing complexity. We demonstrated both by sensor-level and source-level MSE that the processes of losing complexity is temporally slower and spatially restricted for successful inhibition, and is temporally quicker and spatially extensive for unsuccessful inhibition.

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Section: Mse Analysismentioning

confidence: 99%

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Huang

Tseng²,

Liang

2015

Entropy

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“…Frequencies outside this range can be regarded as noise. EEG signals are easily interfered by noise from equipment used during surgery (e.g., from electromyography (EMG), electrooculography (EOG) and electrosurgical units (ESUs)) [9,18]. These will affect the accuracy when the anaesthetist evaluates the DoA of the patient.…”

Section: Step 3 Cut Off the Noise Of Each Imfmentioning

confidence: 99%

“…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. Empirical mode decomposition (EMD) is a method for analyzing non-stationary and nonlinear data such as analog as well as digitized signals, representing time-varying or spatially varying physical quantities [6][7][8][9]. The process involves decomposing these signals into several intrinsic mode functions (IMF), which are simple oscillatory functions with varying amplitude and frequency.…”

Section: Introductionmentioning

confidence: 99%

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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“…Some relevant contributions have proposed using some different techniques, such as the adaptive filtering [4], wavelet transform [5][6], EMD [7] and EEMD [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

Ghosh¹,

Poonia²

2015

IJCA

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Baseline wander (BW) is a low frequency artifact in biomedical electronic recordings. It is usually caused by patient's respiration or movement of equipments. The removal of this artifact is important in ECG recordings for reliable visual interpretation. This paper presents the implementation of Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and EMD based method to remove this disturbance. The EMD based technique serves as an efficient method to remove baseline wander with minimum signal distortion. The results highlights the main differences among all different methods and also show that the EMD based technique is able to remove best baseline wander.

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Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison

Cited by 175 publications

References 27 publications

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

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

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

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