signal processing methods
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Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.
In this time, electrocardiography (ECG) is one of the most useful medical methods, how to diagnose functions of hearth. Th is gives us information about the electrical activity of the heart over time. Th is project is interested in the process and views of the ECG signal, which was measured on the human body via the biosignal amplifi er. Th is signal is processed with the virtual instrumentation machine. Whole system is made by the National Instruments LabVIEW soft ware. Th e goal of this work was to make the quality system, which will process the signal in real-time and were the user can set the main characteristics and parameters for the process – parameters of fi ltrations, type of the transforms, etc.
Analytical devices that combine sensitive biological component with a physicochemical detector hold a great potential for various applications, e.g., environmental monitoring, food analysis or medical diagnostics. Continuous efforts to develop inexpensive sensitive biodevices for detecting target substances typically focus on the design of biorecognition elements and their physical implementation, while the methods for processing signals generated by such devices have received far less attention. Here, we present fundamental considerations related to signal processing in biosensor design and investigate how undemanding signal treatment facilitates calibration and operation of enzyme-based biodevices. Our signal treatment approach was thoroughly validated with two model systems: (i) a biodevice for detecting chemical warfare agents and environmental pollutants based on the activity of haloalkane dehalogenase, with the sensitive range for bis(2-chloroethyl) ether of 0.01-0.8 mM and (ii) a biodevice for detecting hazardous pesticides based on the activity of γ-hexachlorocyclohexane dehydrochlorinase with the sensitive range for γ-hexachlorocyclohexane of 0.01-0.3 mM. We demonstrate that the advanced signal processing based on curve fitting enables precise quantification of parameters important for sensitive operation of enzyme-based biodevices, including: (i) automated exclusion of signal regions with substantial noise, (ii) derivation of calibration curves with significantly reduced error, (iii) shortening of the detection time, and (iv) reliable extrapolation of the signal to the initial conditions. The presented simple signal curve fitting supports rational design of optimal system setup by explicit and flexible quantification of its properties and will find a broad use in the development of sensitive and robust biodevices.
- MeSH
- biosenzitivní techniky metody MeSH
- chemické bojové látky analýza MeSH
- chlorované uhlovodíky analýza MeSH
- enzymy metabolismus MeSH
- ether analogy a deriváty analýza MeSH
- hexany analýza MeSH
- hydrolasy metabolismus MeSH
- kalibrace MeSH
- látky znečišťující životní prostředí analýza MeSH
- lyasy metabolismus MeSH
- počítačové zpracování signálu * MeSH
- senzitivita a specificita MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- Bayesova věta MeSH
- financování organizované MeSH
- Fourierova analýza MeSH
- lidé MeSH
- magnetická rezonanční spektroskopie metody přístrojové vybavení MeSH
- magnetická rezonanční tomografie metody přístrojové vybavení využití MeSH
- počítačové zpracování signálu MeSH
- statistika jako téma MeSH
- Check Tag
- lidé MeSH
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
- MeSH
- algoritmy * MeSH
- elektrokardiografie * MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- srdce MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.
- MeSH
- algoritmy MeSH
- analýza hlavních komponent MeSH
- elektrody MeSH
- elektrokardiografie metody MeSH
- lidé MeSH
- plod fyziologie MeSH
- počítačové zpracování signálu * MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
The quality of data measured in in vivo MR spectroscopy is often insufficient due to a number of limitations such as low concentrations of observed metabolites and restricted measurement time resulting in a low signal-to-noise ratio. However, there are a variety of methods called post-processing techniques which allow the enhancement of the measured signal after measurement. In this review an introduction to the most important post-processing techniques for (1)H MR spectroscopy is given and practical examples are shown. In the first section the concept of FID and spectrum is introduced and the relationship between FID and spectrum is explained. Subsequently, the objectives and description of the following post-processing techniques are provided: eddy current correction, removal of an unwanted component (water), signal filtering for various purposes, zero filling, phase correction and baseline correction.
- MeSH
- algoritmy MeSH
- artefakty MeSH
- lidé MeSH
- magnetická rezonanční spektroskopie metody MeSH
- počítačové zpracování obrazu metody MeSH
- počítačové zpracování signálu MeSH
- tělesná voda metabolismus MeSH
- vylepšení obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- přehledy MeSH
Lots of brain diseases are recognized by EEG recording. EEG signal has a stochastic character, this stochastic nature makes the evaluation of EEG recording complicated. Therefore we use automatic classification methods for EEG processing. This methods help the expert to find significant or physiologically important segments in the EEG recording. The k-means algorithm is a frequently used method in practice for automatic classification. The main disadvantage of the k-means algorithm is the necessary determination of the number of clusters. So far there are many methods which try to determine optimal number of clusters for k-means algorithm. The aim of this study is to test functionality of the two most frequently used methods on EEG signals, concretely the elbow and the silhouette method. In this feasibility study we compared the results of both methods on simulated data and real EEG signal. We want to prove with the help of an expert the possibility to use these functions on real EEG signal. The results show that the silhouette method applied on EEG recordings is more time-consuming than the elbow method. Neither of the methods is able to correctly recognize the number of clusters in the EEG record by expert evaluation and therefore it is not applicable to the automatic classification of EEG based on k-means algorithm.
- MeSH
- algoritmy MeSH
- elektroencefalografie * metody MeSH
- lidé MeSH
- počítačová simulace MeSH
- počítačové zpracování signálu MeSH
- shluková analýza MeSH
- výzkum MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
