Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the “wavelet enhanced” ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.

Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GA-BAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures (Shannon WE (SWE), Tsallis WE (TWE) and Renyi WE (RWE)), Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures (Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)). Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD) modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA. Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

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EEG entropy measures in anesthesia

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain

The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.

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

Electroencephalographic (EEG) recordings are employed in order to investigate the brain activity in neuro pathological subjects. Unfortunately EEG are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper we propose a multiresolution analysis, based on EEG wavelet processing, to extract the cerebral EEG rhythms. We also present a method based on Renyi's entropy and kurtosis to automatically identify the Wavelet components affected by artifacts. Finally, we discuss as the joint use of wavelet analysis, kurtosis and Renyi's entropy allows for a deeper investigation of the brain activity and we discuss the capability of this technique to become an efficient preprocessing step to optimize artifact rejection from EEG. This is the first technique that exploits the peculiarities of EEG to optimize EEG artifact detection.

Brain Activity Investigation by EEG Processing: Wavelet Analysis, Kurtosis and Renyi's Entropy for Artifact Detection

The joint study of respiratory and cardiac activity suggests indirect methods to derive the respiratory signal by electrocardiogram (ECG) processing. Potential advantages of such methods are low cost, high convenience, and continuous noninvasive respiratory monitoring. Recent works show that the respiratory signal can be accurately evaluated by single-channel ECG processing. The aim of this paper is to introduce a new method based on the Empirical Mode Decomposition (EMD) for the respiratory signal evaluation. A comparison versus popular algorithms for the respiratory signal extraction is also shown. Preliminary results confirm that EMD algorithm provides better performances, with respect to others, especially in the case of respiratory waveform reconstruction.

ECG-derived respiratory signal using Empirical Mode Decomposition

PCA–ICA for automatic identification of critical events in continuous coma-EEG monitoring

The aim of fetal monitoring is to identify fetuses at risk of an adverse outcome based on the ability to understand how the fetus reacts to stress before it becomes compromised. Recent works have shown that the ST waveform of the fetal electrocardiogram (fECG) provides continuous information on the capacity of the fetal heart muscle to respond to the stress of labour. The fECG can be ob- tained by a non-invasive technique that consists in collecting elec- trical signals by some sensors on the body of the mother. Unfortu- nately the fetal heartbeat signal yielded by this recording technique is quite weaker than the mother heartbeat signal, also due to the atten- uation during the propagation caused by the tissues; moreover, many other artifacts are superimposed to the two heartbeats. In this paper we propose a method based on wavelet decomposition and indepen- dent component analysis in order to approximate the real shape of the fECG. We also try to evaluate the ST waveform for fetal monitoring.

Independent component and wavelet analysis for fECG extraction: the stwaveform evaluation

Epileptic seizures seem to result from an abnormal synchronization of different areas of the brain, as if a kind of recruitment occurred from a critical area towards other areas of the brain, until the brain can no longer bear the extent of this recruitment and it triggers the seizure in order to reset this abnormal condition. In order to catch these recruitment phenomena, a technique based on entropy is introduced to study the synchronization of the electric activity of neuronal sources in the brain. This technique was tested over 25 EEG dataset from patients affected by absence seizures as well as on 40 EEG dataset from healthy subjects. The results show an abnormal coupling among the electrodes that will be involved in seizure development can be hypothesized before the seizure itself, in particular, the frontal/temporal area appears steadily associated to an underlying high synchrony in absence seizure patients.

F. La Foresta

Papers

Brain Activity Investigation by EEG Processing: Wavelet Analysis, Kurtosis and Renyi's Entropy for Artifact Detection

ECG-derived respiratory signal using Empirical Mode Decomposition

PCA–ICA for automatic identification of critical events in continuous coma-EEG monitoring

Independent component and wavelet analysis for fECG extraction: the stwaveform evaluation

Algorithms and topographic mapping for epileptic seizures recognition and prediction