signal processing Dotaz Zobrazit nápovědu
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.
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
- Klíčová slova
- bioelectrical signals, brain signals, electrocorticography, electroencephalography, signal processing methods,
- MeSH
- mozek MeSH
- počítačové zpracování signálu * MeSH
- vlnková analýza * 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).
- Klíčová slova
- biomedical signals, electrogastrography, electrohysterography, electromyography, electroneurography, electrooculography, electroretinography, signal processing,
- MeSH
- elektromyografie MeSH
- elektrookulografie MeSH
- elektroretinografie * MeSH
- počítačové zpracování signálu * 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.
- Klíčová slova
- digital signal processing, fetal electrocardiogram extraction, fetal monitoring, non-adaptive filtering,
- 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
In this paper, a second-order asynchronous delta-sigma modulator (ADSM) is proposed based on the active-RCintegrators. The ADSM is implemented in the 0.18 μ m CMOS Logic or Mixed-Signal/RF, General Purpose process from the Taiwan Semiconductor Manufacturing Company with a center frequency of 848 kHz at a supply voltage of 1 V with a 92 dB peak signal-to-noise and distortion ratio ( S N D R ), which corresponds to 15 bit resolution. These parameters were achieved in all the endogenous bioelectric signals bandwidth of 10 kHz. The ADSM dissipated 295 μ W and had an area of 0.54 mm 2 . The proposed ADSM with a high resolution, wide bandwidth, and rail-to-rail input voltage range provides the universal solution for endogenous bioelectric signal processing.
- Klíčová slova
- asynchronous delta-sigma modulator (ADSM), biomedical signals, biosensors, center frequency, operational amplifier,
- MeSH
- počítačové zpracování signálu * MeSH
- polovodiče * MeSH
- poměr signál - šum MeSH
- Publikační typ
- dopisy MeSH
- Geografické názvy
- Taiwan MeSH
The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 × 10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.
The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically.
- Klíčová slova
- algorithm, diagnostics, impeller blade, monitoring, signal processing, steam turbine, vibration,
- MeSH
- algoritmy MeSH
- chirurgické nástroje MeSH
- pára * MeSH
- počítačové zpracování signálu MeSH
- vibrace * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- pára * 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.
- Klíčová slova
- biomedical signals, cardiac signals, electrocardiography, fetal electrocardiography, vectorcardiography,
- 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
The rising demand for reliable, real-time, low-maintenance, cost-efficient monitoring systems with a high accuracy is becoming increasingly more notable in everyday life [...].
- MeSH
- ambulantní monitorování * MeSH
- počítačové zpracování signálu * MeSH
- Publikační typ
- úvodníky MeSH
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
- Názvy látek
- 1-chlorohexane MeSH Prohlížeč
- bis(2-chloroethyl)ether MeSH Prohlížeč
- chemické bojové látky MeSH
- chlorované uhlovodíky MeSH
- dehydrochlorinases MeSH Prohlížeč
- enzymy MeSH
- ether MeSH
- haloalkane dehalogenase MeSH Prohlížeč
- hexany MeSH
- hydrolasy MeSH
- látky znečišťující životní prostředí MeSH
- lyasy MeSH
