Empirical mode decomposition applied for non-invasive electrohysterograhic signals denoising.

The electrical activity of the uterus, i.e. the electrohysterogram (EHG), is one of the most prominent tool for preterm labour. There is no standard acquisition set up and often the EHG is corrupted with different types of noise: maternal and fetal electrocardiogram (mECG, fECG), electrical activity of the skeletal muscles, movement artifacts, power line interference (PLI) etc. Moreover, some of these noises overlap in frequency domain with the EHG. Thus, simple linear filtering approaches are not adequate. In this paper the empirical mode decomposition (EMD), a simple and data driven method, is proposed for EHG denoising. The method is evaluated on simulated data having different signal to noise ratios (SNRs) obtaining promising results.

Recording system and data fusion algorithm for enhancing the estimation of the respiratory rate from photoplethysmogram.

The respiratory rate is a vital parameter that can provide valuable information about the health condition of a patient. The extraction of respiratory information from photoplethysmographic signal (PPG) was actually encouraged by the reported results, our main goal being to obtain accurate respiratory rate estimation from the PPG signal. We developed a fusion algorithm that identifies the best derived respiratory signals, from which is possible to extract the respiratory rate; based on these, a global respiratory rate is computed using the proposed fusion algorithm. The algorithm is qualitatively tested on real PPG signals recorded by an acquisition system we implemented, using a reflection pulse oximeter sensor. Its performance is also statistically evaluated using benchmark dataset publically available from CapnoBase.Org.

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure.

Abstract An innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence. A surrogate data approach was adapted to test for statistical significance of time-variant coherences. Using this processing scheme, significant coherence values between a HRV low-frequency sub-band (0.08-0.12 Hz) and the EEG δ envelope (1.5-4 Hz) occurring both in the preictal and early postictal periods of a seizure can be shown. Investigations were performed for all electrodes at 20-s intervals and for selected electrode pairs (T3÷C3, T4÷C4) in a time-variant mode. Synchronization was more pronounced in the group of right hemispheric TLE patients than in the left hemispheric group. Such a group-specific augmentation of synchronization confirms the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the low-frequency HRV components.

Fetal ECG extraction from abdominal signals: a review on suppression of fundamental power line interference component and its harmonics.

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

Fetal electrocardiogram enhancement in abdominal recordings: recording setup analysis.

The fetal electrocardiogram (fECG) obtained from the abdominal signals, to monitor the wellbeing of the fetus, is a weak signal, recorded by placing electrodes on the maternal abdomen surface. When recording the abdominal fECG, the main problem is to separate the fECG from the background noise, including the maternal electrocardiogram (mECG) and/or the power line interference (PLI), this leading to an improved fECG signal to noise ratio (SNR). This paper proposes and evaluates three types of recording configurations, having different reference location, and analyzes the performance of each recording setup, based on the corresponding SNRs, quantitatively evaluated. The fECG extraction is carried out in order to evaluate the performance of each proposed configuration.

Monitoring the fetal heart rate variations by means of time-variant multivariate analysis.

The analysis of the fetal heart rate (fHR) is important in detecting the fetal distress related with hypoxic episodes, noticed sometimes during the uterine activity, which can severely affect the fetus. Occasional synchrony between the fHR and the maternal heart rate (mHR) was reported and the mHR shows some variations during pregnancy and labor, especially when the contractions are very strong. The current study proposes a new strategy to investigate the relations between the fHR, the mHR and the uterine activity, by applying the time-variant Partial Directed Coherence (tvPDC).

Abdominal fetal ECG enhancement by event synchronous canceller.

Abdominal signals (ADS) recorded from pregnant women represent an important tool for monitoring the fetal heart rate (FHR) variability and the well-being state of the fetus, mainly because it has the advantage of being noninvasive. Thus, no risk is given during recording either for the mother or for the fetus, but complex signal processing steps are necessary, mainly due to the presence of the maternal ECG in the ADS, in order to achieve a clean fetal electrocardiogram (fECG). The paper presents an improved application of the Event Synchronous Canceller (ESC) for maternal electrocardiogram (mECG) suppression. An adaptive mECG template which also includes the P and T waves is considered for the ESC algorithm. ESC is able to perfectly separate the mECG even though the fetal beats overlap with the maternal QRS complex (mQRS). The algorithm is applied both on real ADS recorded during pregnancy and on simulated ADS data; the latter now uses simulation of all signal and noise components, including the fetal and maternal ECG. The modified ESC shows good results in extracting a cleaned fECG signal that can help in reducing the inconsistency in interpretation of FHR. Thus the false positive diagnosis regarding the health status of fetus is strongly reduced.

A Self Organizing Map approach to breast cancer detection.

Detection and characterization of cancer tumors in mammograms is vital in daily clinical practice. The problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and on the other hand to the poorly defined border between the healthy and the cancerous zone. Even though it has been studied for many years, there are still remaining challenges and directions for future research such as developing better enhancement and segmentation algorithms. The performance of the Self Organizing Map (SOM) in detecting the cancer suspicious regions in digitized mammograms is revealed in this study. In order to achieve the best results we firstly apply the preprocessing algorithms proposed in section II of the study.

Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique.

Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

EEG features extraction for motor imagery.

Motor imagery is the mental simulation of a motor act that includes preparation for movement, passive observations of action and mental operations of motor representations implicitly or explicitly. Motor imagery as preparation for immediate movement likely involves the motor executive brain regions. Implicit mental operations of motor representations are considered to underlie cognitive functions. Another problem concerning neuro-imaging studies on motor imagery is that the performance of imagination is very difficult to control. The ability of an individual to control its EEG may enable him to communicate without being able to control their voluntary muscles. Communication based on EEG signals does not require neuromuscular control and the individuals who have neuromuscular disorders and who may have no more control over any of their conventional communication abilities may still be able to communicate through a direct brain-computer interface. A brain-computer interface replaces the use of nerves and muscles and the movements they produce with electrophysiological signals and is coupled with the hardware and software that translate those signals into physical actions. One of the most important components of a brain-computer interface is the EEG feature extraction procedure. This paper presents an approach that uses self-organizing fuzzy neural network based time series prediction that performs EEG feature extraction in the time domain only. EEG is recorded from two electrodes placed on the scalp over the motor cortex. EEG signals from each electrode are predicted by a single fuzzy neural network. Features derived from the mean squared error of the predictions and from the mean squared of the predicted signals are extracted from EEG data by means of a sliding window. The architecture of the two auto-organizing fuzzy neural networks is a network with multi inputs and single output.

Improved method for fetal heart rate monitoring.

The fetal ECG can be detected in the recorded abdominal signals. A new procedure to compute the fetal heart rate (FHR) is proposed. The abdominal signal is first preprocessed in order to remove the baseline and the uterine contractions. Then the ECG of the mother (MECG) is removed using coherent averaging and optimizing the averaged MECG template. The channels containing the clearest fetal ECG signal (FECG) are identified by the autocorrelation function. The FECG is enhanced by the cross correlation between the two channels that show the strongest FECG. This enhancement is possible since the residual noise in the abdominal signal after removal of baseline, uterine contractions and maternal ECG is not correlated among the channels. The fetal R-Peaks are then detected and the FHR is computed. The obtained FHR is further corrected, using the information about the MECG and about the FECG.