1,721,066 research outputs found
Il fenomeno dell’auto-organizzazione del movimento: l’analisi della coordinazione bimanuale delle dita attraverso la risonanza magnetica funzionale (fMRI)
No full text
Métayers, bêche et climat : la plaine de Bologne, 1718-1774
No full textFinzi Roberto, Comani Silvia. Métayers, bêche et climat : la plaine de Bologne, 1718-1774. In: Revue d’histoire moderne et contemporaine, tome 31 N°3, Juillet-septembre 1984. pp. 472-488
Digital pre-processing of foetal magnetocardiographic signals for optimal extraction of foetal traces
No full textEditorial: Bridging the gap before and after birth: methods and technologies to explore the functional neural development in humans
No full text
Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data
No full textEEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications
Fetal cardiac time intervals: validation of an automatic tool for beat-to-beat detection on fetal magnetocardiograms
No full textFetal magnetocardiography (fMCG) allows the
non-invasive registration of fetal cardiac activity. This
technique, combined with the use of independent component
analysis (ICA) for signal processing, allows reconstructing of
reliable fetal cardiac traces. Low noise fetal signals can be used
to evaluate fetal cardiac time intervals (fCTI), useful to
monitor fetal heart function. In this work we present a method
for the automatic detection of cardiac waves (ACWD); it was
validated on 45 fMCG data sets of normal fetuses with
gestational age from 22 to 37 weeks. The outcomes of the
automatic procedure were compared with those of a manual
procedure performed by three independent operators on
rhythm strips of 100 consecutive cardiac cycles for each data
set. Distances between the wave boundaries detected with the
two methods were statistically estimated using confidence
intervals: differences were always comparable to those that
could be obtained from different investigators’ estimates.
Statistical correlation between fCTI quantified with ACWD
and with a manual procedure was assessed using the
parametric two-tailed Pearson’s correlation test, significance
level at a = 0.01. The automatic procedure showed a
computation time decrease in the ratio of approximately 1:600 with respect to the manual procedure performed on the same number of beats
ECG compression by efficient coding
No full textThe continuous demand for high performance and low cost electrocardiogram (ECG) processing systems have required the elaboration of more and more efficient and reliable ECG compression techniques. Such techniques face a tradeoff between compression ratio and retrieved quality, where the decrease of the last can compromise the subsequent use of the signal for clinical purposes. The objective of this work is to evaluate the validity and performance of an independent component analysis (ICA) based scheme used to efficiently compress ECC signals while introducing tests for a different type of record of the electrical activity of the heart, such as fetal magnetocardiogram (fMCG). As a result, the reconstructed signals underwent negligible visual deterioration, while achieving promising compression ratios
Independent Component Analysis (ICA) for the reconstruction of reliable fetal magnetocardiograms
No full text- …
