Principal component analysis
Dotaz
Zobrazit nápovědu
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.
Contemporary descriptions of motor control suggest that variability in movement can be indicative of skilled or unskilled performance. Here we used principal component analysis to study the kicking performance of elite and sub-elite soldiers who were highly familiar with the skill in order to compare the variability in the first and second principal components. The subjects kicked a force plate under a range of loaded conditions, and their movement was recorded using optical motion capture. The first principal component explained >92% of the variability across all kinematic variables when analyzed separately for each condition, and both groups and explained more of the variation in the movement of the elite group. There was more variation in the loading coefficient of the first principal component for the sub-elite group. In contrast, for the second principal component, there was more variation in the loading coefficient for the elite group, and the relative magnitude of the variation was greater than for the first principal component for both groups. These results suggest that the first principal component represented the most fundamental movement pattern, and there was less variation in this mode for the elite group. In addition, more of the variability was explained by the hip than the knee angle entered when both variables were entered into the same PCA, which suggests that the movement is driven by the hip.
- MeSH
- analýza hlavních komponent MeSH
- biomechanika MeSH
- dolní končetina * MeSH
- lidé MeSH
- pohyb * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: EEG mu rhythm suppression is assessed in experiments on the execution, observation and imagination of movements. It is utilised for studying of actions, language, empathy in healthy individuals and preservation of sensorimotor system functions in patients with schizophrenia and autism spectrum disorders. While EEG alpha and mu rhythms are recorded in the same frequency range (8-13 Hz), their specification becomes a serious issue. THE NEW METHOD: is based on the spatial and functional characteristics of the mu wave, which are: (1) the mu rhythm is located over the sensorimotor cortex; (2) it desynchronises during movement processing and does not respond on the eyes opening. In EEG recordings, we analysed the mu rhythm under conditions with eyes opened and eyes closed (baseline), and during a motor imagery task with eyes closed. EEG recordings were processed by principal component analysis (PCA). RESULTS: The analysis of EEG data with the proposed approach revealed the maximum spectral power of mu rhythm localised in the sensorimotor areas. During motor imagery, mu rhythm was suppressed more in frontal and central sites than in occipital sites, whereas alpha rhythm was suppressed more in parietal and occipital sites. Mu rhythm desynchronization in sensorimotor sites during motor imagery was greater than alpha rhythm desynchronization. The proposed method enabled EEG mu rhythm separation from its mix with alpha rhythm. CONCLUSIONS: EEG mu rhythm separation with the proposed method satisfies its classical definition.
- MeSH
- alfa rytmus EEG * MeSH
- analýza hlavních komponent MeSH
- elektroencefalografie * MeSH
- imaginace MeSH
- lidé MeSH
- pohyb MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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
- algoritmy MeSH
- elektrokardiografie MeSH
- financování organizované MeSH
- hodnotící studie jako téma MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- nemoci autonomního nervového systému patofyziologie prevence a kontrola MeSH
- senioři MeSH
- srdce inervace MeSH
- srdeční frekvence fyziologie MeSH
- syndrom chronické únavy diagnóza prevence a kontrola MeSH
- syndrom posturální ortostatické tachykardie diagnóza prevence a kontrola MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- senioři MeSH
- Publikační typ
- práce podpořená grantem MeSH
Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
- MeSH
- analýza hlavních komponent * MeSH
- bipolární porucha * diagnostické zobrazování farmakoterapie patologie MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mladý dospělý MeSH
- mozek diagnostické zobrazování patologie MeSH
- mozková kůra diagnostické zobrazování patologie MeSH
- obezita * diagnostické zobrazování MeSH
- schizofrenie diagnostické zobrazování patologie farmakoterapie patofyziologie MeSH
- shluková analýza MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- MeSH
- analýza hlavních komponent metody MeSH
- elektrokardiografie ambulantní metody přístrojové vybavení využití MeSH
- komorové extrasystoly diagnóza MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- programovací jazyk MeSH
- software MeSH
- srdeční arytmie diagnóza MeSH
- statistika jako téma metody MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
RATIONALE: Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. METHODS: Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. RESULTS: For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. CONCLUSIONS: Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available free of charge from http://uprt.vscht.cz/ms.
- MeSH
- analýza hlavních komponent * MeSH
- bakteriologické techniky MeSH
- časové faktory MeSH
- Cronobacter sakazakii růst a vývoj metabolismus MeSH
- kultivační média MeSH
- software * MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice metody normy statistika a číselné údaje MeSH
- Publikační typ
- časopisecké články MeSH
Data segmentation and object rendering is required for localization super-resolution microscopy, fluorescent photoactivation localization microscopy (FPALM), and direct stochastic optical reconstruction microscopy (dSTORM). We developed and validated methods for segmenting objects based on Delaunay triangulation in 3D space, followed by facet culling. We applied them to visualize mitochondrial nucleoids, which confine DNA in complexes with mitochondrial (mt) transcription factor A (TFAM) and gene expression machinery proteins, such as mt single-stranded-DNA-binding protein (mtSSB). Eos2-conjugated TFAM visualized nucleoids in HepG2 cells, which was compared with dSTORM 3D-immunocytochemistry of TFAM, mtSSB, or DNA. The localized fluorophores of FPALM/dSTORM data were segmented using Delaunay triangulation into polyhedron models and by principal component analysis (PCA) into general PCA ellipsoids. The PCA ellipsoids were normalized to the smoothed volume of polyhedrons or by the net unsmoothed Delaunay volume and remodeled into rotational ellipsoids to obtain models, termed DVRE. The most frequent size of ellipsoid nucleoid model imaged via TFAM was 35 × 45 × 95 nm; or 35 × 45 × 75 nm for mtDNA cores; and 25 × 45 × 100 nm for nucleoids imaged via mtSSB. Nucleoids encompassed different point density and wide size ranges, speculatively due to different activity stemming from different TFAM/mtDNA stoichiometry/density. Considering twofold lower axial vs. lateral resolution, only bulky DVRE models with an aspect ratio >3 and tilted toward the xy-plane were considered as two proximal nucleoids, suspicious occurring after division following mtDNA replication. The existence of proximal nucleoids in mtDNA-dSTORM 3D images of mtDNA "doubling"-supported possible direct observations of mt nucleoid division after mtDNA replication.
- MeSH
- algoritmy * MeSH
- analýza hlavních komponent * MeSH
- buňky Hep G2 MeSH
- DNA vazebné proteiny metabolismus MeSH
- fluorescenční mikroskopie * MeSH
- konformace nukleové kyseliny MeSH
- lidé MeSH
- mitochondriální DNA chemie metabolismus MeSH
- mitochondriální proteiny metabolismus MeSH
- molekulární modely MeSH
- zobrazování trojrozměrné * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
