independent component analysis
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Jedním z mnoha problémů elektroencefalografie (EEG) je analýza aktivity mozkové činnosti z měřených dat, která mohou být zkreslena různými poruchami-artefakty. V tomto článku jsme se zaměřili na ověření vlastností dvou metod - analýzy nezávislých komponent (Independent Component Analysis, ICA) a analýzy hlavních komponent (Principal Component Analysis, PCA), které mohou tyto artefakty eliminovat. Metody jsme aplikovali na reálná EEG data, která byla kontaminována amplitudovým a síťovým artefaktem. Cílem bylo zjistit, do jaké míry jsou tyto metody schopny znehodnocené signály rekonstruovat do diagnosticky vyuŽitelné podoby. Výsledky jsme u obou metod vzájemně porovnali, včetně ověření shody s názorem lékaře.
Extraction of the meaningful brain activity informationfrom measured signals distorted by various artifacts is a practical problem in electroencephalography (EEG). Eye movements, muscle activity and mechanical and electrical displacements in the measuring apparatus represent typical artefacts. Several methods were developed for removing these artifacts. Two of them - Independent Component Analysis (ICA) and Principal Component Analysis (PCA) are discussed in this paper. Both ICA and PCA are useful in signal description, optimal feature extraction, and data compression. We would like to show that ICA and PCA could as well effectively separate and remove contamination from a wide variety of artifactual sources in EEG records. Results obtained using ICA with those from PCA are compared.
Adaptive and learning systems for sygnal processing, communications, and control
481 s.
Beat detection is a basic and fundamental step in electrocardiogram (ECG) processing. In many ECG applications strong artifacts from biological or technical sources could appear and cause distortion of ECG signals. Beat detection algorithm desired property is to avoid these distortions and detect beats in any situation. Our developed method is an extension of Christov's beat detection algorithm, which detects beat using combined adaptive threshold on transformed ECG signal (complex lead). Our offline extension adds estimation of independent components of measured signal into the transformation of ECG creating a signal called complex component, which enhances ECG activity and enables beat detection in presence of strong noises. This makes the beat detection algorithm much more robust in cases of unpredictable noise appearances, typical for holter ECGs and telemedicine applications of ECG. We compared our algorithm with the performance of our implementation of the Christov's and Hamilton's beat detection algorithm.
We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.
Na kazuistické studii nemocného s obsedantně-kompulzivní poruchou (OCD) je demonstrováno diagnostické a terapeutické využití informace elektrické aktivity mozku (EEG) rozložené metodou analýzy nezávislých komponent (ICA). Klidový EEG záznam pacienta byl analyzován pomocí sLORETA (standardized low-resolution electromagnetic tomography) a pomocí analýzy nezávislých komponent v softwaru Independent Component Neurofeedback (ICoN, Nova Tech EEG, Inc). Srovnání s sLORETA normativní databází (Nova Tech EEG, Inc) ukázalo, že pacient má zvýšené množství absolutní theta aktivity lokalizované zejména v předním cingulu a orbitofrontální kůře, jejichž úloha v patofyziologii OCD byla v minulosti popsána. Abnormní theta vlny byly patrné i při vizuálním hodnocení EEG záznamu. Analýza nezávislých komponent ukázala, že jejich hlavní zdroj je lokalizován v afektivní části předního cingula a mediální orbitofrontální kůře. Je racionální předpokládat, že neurofeedback zaměřený na snižování aktivity tohoto zdroje v pásmu theta by mohl vést k normalizaci dysfunkční neuronální sítě a ke zlepšení klinických symptomů.
We demonstrate the potential diagnostic and therapeutic use of electrical brain activity information decomposed via independent component analysis (ICA ) in an obsessive-compulsive patient. The resting EEG was analyzed by sLORETA (standardized low-resolution electromagnetic tomography) and by the ICA using the Independent Component Neurofeedback software (ICoN, Nova Tech EEG, Inc). The sLORETA normative database comparison (Nova Tech EEG, Inc) revealed increase of absolute power in the theta frequency band, especially in the anterior cingulate and orbitofrontal gyrus whose involvement in OCD pathophysiology has been previously reported. Abnormal theta waves were also detectable by visual EEG inspection. The ICA identified their main source, localized in the affective part of the anterior cingulate and in the medial orbitofrontal cortex. With respect to our findings we hypothesize that neurofeedback aimed at decreasing
- MeSH
- dospělí MeSH
- elektroencefalografie metody využití MeSH
- financování organizované MeSH
- interpretace obrazu počítačem metody využití MeSH
- lidé MeSH
- obsedantně kompulzivní porucha diagnóza terapie MeSH
- theta rytmus EEG metody využití MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records. We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning. We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately. Due to a reduced dimension of the subspaces, the overlearning is suppressed, and muscular artifacts are better separated. Once the muscular artifacts are removed, the signals in the individual subspaces are combined to provide an artifact free EEG record. We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal. The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
- MeSH
- algoritmy * MeSH
- artefakty * MeSH
- elektroencefalografie * metody MeSH
- elektromyografie metody MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- počítačové zpracování signálu * MeSH
- přeučení MeSH
- svaly fyziologie MeSH
- Check Tag
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- hodnotící studie MeSH
- práce podpořená grantem MeSH
The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother's chest.
