1,721,147 research outputs found
Fetal heart rate monitoring during pregnancy for assessing the well-being of the fetus
Full text linkLong-term fetal heart rate (FHR) monitoring is necessary to ensure that any FHR abnormality, which may appear at any time
during pregnancy and labor, can be detected. An ambulatory electrocardiogram (ECG) recorder employing three abdominal
surface electrodes has been developed towards achieving such a monitoring. The difficulties encountered in determining the
FHR from the maternal abdominal signal are mainly the interference due to the electromyogram and motion artifact, and
relatively small amplitude of the fetal ECG compared to that of the maternal. Thus improvement to existing abdominal signal
processing algorithm is necessary to increase the percentage of successful monitoring. A real-time algorithm has been developed
for the simultaneous measurement of the fetal and maternal heart rates from the abdominal signal. The algorithm is based on
digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima. A
filtering technique has been utilized in the proposed algorithm to extract the fetal signal from the maternal abdominal signal.
This is an alternative to a previous method which subtracts the maternal complexes from the abdominal signal with a need to
overcome the problem of matching a template to the complexes. The proposed algorithm is capable of continuous ambulatory
FHR monitoring either off-line, by using recorded signals, or on-line by a clinician during antenatal examination. The
performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. The resulting
average accuracy is 83% for the FHR detection. The detection of the FHR from the maternal abdominal signal by the developed
algorithm has also been compared with a short-term monitoring commercial instrument IFM-500 for the assessment of the
reliability of the algorithm. The performance achieved from the comparison shows non-significant differences of means, low
error percentages and linear correlation coefficient. A portable system based on the developed algorithm has the potential for
increased percentage of real-time FHR detection thus enabling successful long-term fetal monitoring
Fetal ECG extraction from maternal abdominal ECG using neural network
Full text linkFECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG
Brain computer interface based wheelchair for disable people using electroencephalography signal
No full textBrain computer interface causes direct operation between brain and computer. Interfacing of the EEG signal produced by the brain with any control or communication device produces unidirectional communicating channel. Among the non-invasive techniques for probing human brain dynamics, EEG provides a direct measurement of cortical activity i.e., intention of a human being; with millisecond temporal resolution. However, the well-off interconnectivity between the various cortical areas may allow for events in one area to be preceded or accompanied by detectable patterns in other unrelated areas. To develop a practical BCI system, three components should be considered. These are i) to establish an appropriate multivariate signal processing technique to extract multiclass features from multi-channel EEG signals, ii) to look up suitable pattern classification technique to improve the performance of BCI and finally iii) to develop an approprite interfacing circuit to control a user device. Due to poor classification acuracy, practical BCI system has not been fully materialised yet. However, an advanced and simple classification algorithm for motor imagery related BCI system has already been developed with Mahalanobis Discriminant Analysis (MDA) technique. It obtains 93% of kappa accuracy in evaluation phase, which is validated and acceptable, whereas the accuracy with others is maxmimum 86%. Moreover, the developed technique needs a very low computational requirement that makes it suitable for real-time BCI based system to control a wheelchair for the disabled people. To have a fruitful result, the next phase of hardware realization research and interfacing with users are essential which is highly desired factor in a practical/commercial BCI system development
Biomedical signal processing and applications
No full textIn biomedical signal processing, the aim is to extract clinically, biochemically or pharmaceutically relevant
information in order to enable an improved medical diagnosis. All living things, from cells to organism, deliver signals of biological origin. Such signals can be electric, mechanical, or chemical. All such signals can be of interest for diagnosis, for patient monitoring and biomedical research. The main task of processing biomedical signals is to filter the signal of interest out of from the noisy background and to reduce the redundant data stream to only a few,but relevant parameters. This paper will cover biomedical signal processing as used in diagnostic instrumentation. A number of current research projects will also be outlined with emphasis on intelligent medical diagnosis system
Encryption in TECB mode: modeling, simulation and synthesis
No full textThe growth of the Internet as a vehicle for secure communication has resulted in Data Encryption Standard DES) no longer capable of providing high-level security for data protection. Triple Data Encryption Standard (3DES)
is a symmetric block cipher with 192 bits key proposed to further enhance DES. Many applications crave for the speed of a hardware encryption implementation
while trying to preserve the flexibility and low cost of a software implementation. This project used single core module to implement encryption in Triple
DES Electronic Code Book (TECB) mode, which was modeled using hardware description language VHDL. The architecture was mapped in Altera EPF10K100EFC484-1 and EP20K200EFC672-1X for performance investigations and resulted in achieving encryption rate of 102.56 Mbps, area utilization of 2111 logic cells (25%) and a higher maximum operating frequency of 78.59MHz by implementing on the larger FPGA device EP20K200EFC672-1X. It also suggested that 3DES hardware was 2.4 times faster than its software coun-terpart
Simulation of a surface-transverse wave (STW) biosensor for DF-1 cells
Full text linkA 250 MHz Surface-Transverse Wave (STW) resonator is employed as a sensor element for the
detection of DF-1 cells. STW belongs to the shear-horizontal acoustic plate modes (SH-APM) waves’
family where it has attracted plenty considerable interest. STWs are horizontally polarized shear waves
which are generated and detected by the interdigital transducers (IDTs) similar to surface-acoustic wave
(SAW) resonators [1]. Detection of chemical and biological agents in aqueous solutions is a difficult
problem, especially when the detection technique has to be sensitive, power-efficient and very handy.
Acoustic plate mode is a mode of vibration where particle motion is parallel to the surface. This makes it
possible to produce a sensitive sensor capable of operating in fluids [2]. This paper presents the biosensor
prototype utilizing STW resonator
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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