Since the 1970s, various automatic sleep spindles procedures have been implemented and presented in the literature. Unfortunately, their results are not easily comparable because the databases, the assessment methods and the terminologies employed are often radically different. In this study, we propose a systematic assessment method for any automatic sleep spindles detection algorithm. We apply this assessment method to our own automatic detection process in order to illustrate and legitimate its use. We obtain a global sensitivity of 70.20%, for a false positive proportion (relative to the total number of visually scored sleep spindles) of only 26.44% (False positive rate = 1.38% and specificity = 98.62%).

Abstract-In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively with scorer 1 and scorer 2. The false positive proportions (compared to the total number of visually scored K-complexes) are of 19.62% and 181.25%, while the false positive rates estimated on a one 1 second resolution are only of 0.53% and 1.53%. These results suggest that our approach is completely suitable since its performances are similar to those of the human scorers.

-

We introduce a new automatic method to eliminate electrocardiogram (ECG) noise in an electroencephalogram (EEG) or electrooculogram (EOG). It is based on a modification of the independent component analysis (ICA) algorithm which gives promising results while using only a single-channel electroencephalogram (or electrooculogram) and the ECG. To check the effectiveness of our approach, we compared it with other methods, that is, ensemble average subtraction (EAS) and adaptive filtering (AF). Tests were carried out on simulated data obtained by addition of a filtered ECG on a visually clean original EEG and on real data made up of 10 excerpts of polysomnographic (PSG) sleep recordings containing ECG artifacts and other typical artifacts (e.g., movement, sweat, respiration, etc.). We found that our modified ICA algorithm had the most promising performance on simulated data since it presented the minimal root mean-squared error. Furthermore, using real data, we noted that this algorithm was the most robust to various waveforms of cardiac interference and to the presence of other artifacts, with a correction rate of 91.0%, against 83.5% for EAS and 83.1% for AF.

In this paper, we introduce a new automatic method for electrocardiogram (ECG) artifact elimination from the electroencephalogram (EEG) or the electrooculogram (EOG). It is based on a modification of the independent component analysis (ICA) algorithm which gives promising results while only using a single-channel EEG (or EOG) and the ECG. To check the effectiveness of our approach, we compared its correction rate with those obtained by ensemble average subtraction (EAS) and adaptive filtering (AF). For this purpose, we applied these algorithms to 10 excerpts of polysomnographic sleep recordings containing ECG artifacts and other typical artifacts (e.g. movement, sweat, respiration, etc.). Two hundred successive interference peaks were examined in each excerpt to compute correction rates. We found that our modified ICA was the most robust to various waveforms of cardiac interference and to the presence of others artifacts, with a correction rate of 91.0%, against 83.5% for EAS and 83.1% for AF.

No abstract

The electrocardiography (ECG) artifact in surface electromyography (sEMG) is a major source of noise influencing the analyses. Moreover, in many cases the sEMG signal is the only available signal, making this removal more complicated. We compare the performance of two recently described single channel blind source separation methods with the commonly used template subtraction method on both simulations and real-life data. These two methods decompose a single channel recording into a multichannel representation before applying independent component analysis to these multichannel data. The decomposition methods are the wavelet decomposition and ensemble empirical mode decomposition (EEMD). The EEMD based single channel technique shows better performance compared to template subtraction and the wavelet based alternative for both high and low signal-to-artifact ratio and for simulated and real-life data, but at the expense of a higher computational load. We conclude that the EEMD based method has its potential in eliminating spike-like artifacts in electrophysiological signals.