Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01-real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.
- Publikační typ
- časopisecké články MeSH
- Publikační typ
- abstrakt z konference MeSH
1. vyd. 168 s. : il. ; 21 cm
- MeSH
- automatizované zpracování dat MeSH
- elektroencefalografie metody MeSH
- interpretace obrazu počítačem MeSH
- neurofyziologie metody MeSH
- sběr dat MeSH
- spektrální analýza MeSH
- Konspekt
- Informační věda
- NLK Obory
- knihovnictví, informační věda a muzeologie
- neurovědy
- NLK Publikační typ
- učebnice vysokých škol
Elektroencefalogram (EEG) je citlivý ukazatel zrání a vývojových změn mozku u novorozenců a nedonošenců. Tyto změny se odrážejí ve struktuře spánkových stavů. V tomto příspěvku uvádíme novou metodu pro automatizované modelování a detekci změn spánkových stavů u novorozeneckého EEG. Postup je založen na zpracovávání a analýze strukturálních časových profi - lů, získaných pomocí vícekanálové adaptivní segmentace a následné klasifi kace EEG grafoelementů pomocí shlukové analýzy. Profi ly, funkce členství segmentů v příslušné třídě v průběhu času, odrážejí dynamickou strukturu EEG, mohou být využity pro indikaci změny stavu novorozeneckého spánku. Metoda je dostatečně citlivá i pro velmi obtížnou úlohu: modelování mikrostruktury spánku u nejmenších nedonošených dětí.
The electroencephalogram (EEG) is a sensitive marker of brain maturation and developmental changes in term and preterm newborns. A new method for automatic sleep stages detection in neonatal EEG was developed. Th e procedure is based on processing of time profi les computed by adaptive segmentation and subsequent classifi cation of extracted signal graphoelements. Th e time profi les, functions of the class membership in the course of time, refl ect the dynamic EEG structure and may be used for indication of changes in the neonatal sleep. Th e method is suffi ciently sensitive to detect the sleep stages even in preterm infants EEG.
- MeSH
- algoritmy MeSH
- elektroencefalografie přístrojové vybavení využití MeSH
- financování organizované MeSH
- lidé MeSH
- modely neurologické MeSH
- novorozenec nedonošený MeSH
- novorozenec MeSH
- počítačové zpracování signálu přístrojové vybavení MeSH
- shluková analýza MeSH
- spánek fyziologie MeSH
- stadia spánku fyziologie MeSH
- Check Tag
- lidé MeSH
- novorozenec MeSH
The neural network is computational model based on the features abstraction of biological neural systems. Th e neural networks have many ways of usage in technical fi eld. Th ey have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct soft ware agents or autonomous robots. In this paper is described usage of neural networks for ECG signal prediction. Th e ECG signal prediction can be used for automated detection of irregular heartbeat – extrasystole. Th e automated detection system of unexpected abnormalities is also described in this paper.
To an exact diagnosis statement a correctly evaluation of sleep electroencephalogram is required. Th e sleep activity record is very long (a few hours). Th e evaluation of the sleep activity is time-consuming. Th e evaluation provides a doctor. In order to save time various automatic evaluations methods are developed. Th e one of methods includes dividing the signal into 30 seconds long sections. Th ese sections are analyzed. Th e 30 seconds length sections are with adaptive segmentation reduced. From these segments mean amplitude, mean frequency, 1. and 2. derivation are computed. Th e processing and the evaluation of the segments properties leads to defi nition of sleep status.
- MeSH
- algoritmy MeSH
- dítě MeSH
- elektroencefalografie metody přístrojové vybavení využití MeSH
- lidé MeSH
- neuronové sítě (počítačové) MeSH
- počítačové zpracování signálu přístrojové vybavení MeSH
- shluková analýza MeSH
- spánek fyziologie MeSH
- stadia spánku fyziologie MeSH
- statistika jako téma metody MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
The new method for automatic sleep stages detection in neonatal EEG was developed. The procedure is based on processing of time profiles computed by adaptive segmentation and subsequent classification of signal graphoelements. The time profiles, functions of the class membership in the course of time, reflect the dynamic EEG structure and may be used for indication of changes in the neonatal sleep stages.
