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.
- MeSH
- Artifacts MeSH
- Electroencephalography MeSH
- Factor Analysis, Statistical methods MeSH
- Research Support as Topic MeSH
- Publication type
- Review MeSH
Adaptive and learning systems for sygnal processing, communications, and control
481 s.
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.
Širdies veikla yra kontroliuojama vegetacinės nervų sistemos, keičiant širdies ritmo dažnį ir/arba širdies raumens susitraukimų jėgą, pagal viso kūno hemodinaminius poreikius. Reguliavimą atlieka pastoviai konkuruojančios simpatinė ir parasimpatinė nervų sistemos. Šių mechanizmų sutrikimai sukelia ortostatinę tachikardiją ir/arba nuolatinio nuovargio sindromą. Reguliacinių mechanizmų funkcionalumo bei efektyvumo įvertinimas gali suteikti labai vertingos diagnostinės informacijos apie širdies veiklos reguliavimo sutrikimus ankstyvose susirgimų stadijose bei padėti kontroliuoti gijimo procesus, reabilitaciją po intensyvaus gydymo. Kiekybinis EKG P-bangos formų vertinimas ortostatinio mėginio (kuris staiga sutrikdo pusiausvyrą tarp simpatinės ir parasimpatinės nervų sistemos) metu, naudojant pagrindinių komponenčių analizės pagrindu sukurtą metodą, leidžia išskirti kiekybinius širdies veiklos reguliacinių mechanizmų funkcionalumo bei efektyvumo įverčius. Šis metodas galėtų būtų naudojamas e-Sveikatos sistemoje.
Background: Cardiac output is controlled by the autonomic nervous system by changing the heart rate and/or the contractions of the heart muscle in response to the hemodynamic needs of the whole body. Malfunction of these mechanisms causes the postural orthostatic tachycardia syndrome and/or the chronic fatigue syndrome. Evaluation of functionality and efficiency of the control mechanisms could give valuable diagnostic information in the early stages of dysfunction of the heart control systems and help to monitor the healing process in rehabilitation period after interventions. Objectives: In this study we demonstrate how P-wave changes evoked by an orthostatic test could be quantitatively evaluated by using the method based on the principal component analysis. Methods: ECG signals were recorded during an orthostatic test performed according to the typical protocol in three groups of volunteer subjects representing healthy young and older persons, part of which had transient periods of supraventricular arrhythmias. Quantitative evaluation of P-wave morphology changes was performed by means of principal component analysis-based method. Results: Principal component-based estimates showed certain variety of P-wave shape during orthostatic test, what revealed a possibility to evaluate the properties of parasympathetic heart control. Conclusions: Quantitative evaluation of ECG P-wave changes evoked by an orthostatic test by using a newly developed method provides a quantitative estimate for functionality and efficiency of the heart rate control mechanisms. The method could be used in eHealth systems.
- MeSH
- Algorithms MeSH
- Electrocardiography MeSH
- Financing, Organized MeSH
- Evaluation Studies as Topic MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Autonomic Nervous System Diseases physiopathology prevention & control MeSH
- Aged MeSH
- Heart innervation MeSH
- Heart Rate physiology MeSH
- Fatigue Syndrome, Chronic diagnosis prevention & control MeSH
- Postural Orthostatic Tachycardia Syndrome diagnosis prevention & control MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Aged MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Principal Component Analysis methods MeSH
- Electrocardiography, Ambulatory methods instrumentation utilization MeSH
- Ventricular Premature Complexes diagnosis MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted MeSH
- Programming Languages MeSH
- Software MeSH
- Arrhythmias, Cardiac diagnosis MeSH
- Statistics as Topic methods MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
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.
... respiration -- 1.2.2 Blood pressure fluctuation at frequency of respiration -- 1.2.3 Low frequency components ... ... Circulatory rhythms in patients suffering from neurocirculatory asthenia -- 1.3.4 Power spectral analysis ... ... of heart rate variability in patients with cardio vascular disorders -- 1.3.5 Power spectral analysis ... ... Methods -- 2.1 Spectral analysis -- 2.2 Measurement procedures -- 2.2.1 Blood pressure -- 2.2.2 Finger ... ... The application of heart-rate variability analysis in pediatrics -- 11. ...
Opuscula physiologica
125 s. : il.
- MeSH
- Blood Circulation physiology MeSH
- Spectrum Analysis MeSH
- Publication type
- Monograph MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- vnitřní lékařství
- kardiologie
- chemie, klinická chemie
- angiologie
