Abstract: In this paper, algorithm estimating the blood pressure
was proposed using the pulse transit time (PTT) as a more convenient
method of measuring the blood pressure. After measuring ECG and
pressure pulse, and photoplethysmography, the PTT was calculated
from the acquired signals. Thereafter, the system to indirectly measure
the systolic pressure and the diastolic pressure was composed using
the statistic method. In comparison between the blood pressure
indirectly measured by proposed algorithm estimating the blood
pressure and real blood pressure measured by conventional
sphygmomanometer, the systolic pressure indicates the mean error of
±3.24mmHg and the standard deviation of 2.53mmHg, while the
diastolic pressure indicates the satisfactory result, that is, the mean
error of ±1.80mmHg and the standard deviation of 1.39mmHg. These
results are satisfied with the regulation of ANSI/AAMI for
certification of sphygmomanometer that real measurement error value
should be within the mean error of ±5mmHg and the standard
deviation of 8mmHg. These results are suggest the possibility of
applying to portable and long time blood pressure monitoring system
hereafter.
Abstract: In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.
Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: In this paper we present a novel design of a wearable
electronic textile. After defining a special application, we used the
specifications of some low power, tiny elements including sensors,
microcontrollers, transceivers, and a fault tolerant special topology to
have the most reliability as well as low power consumption and
longer lifetime. We have considered two different conditions as
normal and bodily critical conditions and set priorities for using
different sensors in various conditions to have a longer effective
lifetime.
Abstract: In this paper we present a novel approach for wavelet compression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithm on different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression.
Abstract: Electrocardiogram (ECG) segmentation is necessary
to help reduce the time consuming task of manually annotating
ECG-s. Several algorithms have been developed to segment the ECG
automatically. We first review several of such methods, and then
present a new single lead segmentation method based on Adaptive
piecewise constant approximation (APCA) and Piecewise derivative
dynamic time warping (PDDTW). The results are tested on the QT
database. We compared our results to Laguna-s two lead method. Our
proposed approach has a comparable mean error, but yields a slightly
higher standard deviation than Laguna-s method.
Abstract: Noise level has critical effects on the diagnostic
performance of signal-averaged electrocardiogram (SAECG), because
the true starting and end points of QRS complex would be masked by
the residual noise and sensitive to the noise level. Several studies and
commercial machines have used a fixed number of heart beats
(typically between 200 to 600 beats) or set a predefined noise level
(typically between 0.3 to 1.0 μV) in each X, Y and Z lead to perform
SAECG analysis. However different criteria or methods used to
perform SAECG would cause the discrepancies of the noise levels
among study subjects. According to the recommendations of 1991
ESC, AHA and ACC Task Force Consensus Document for the use of
SAECG, the determinations of onset and offset are related closely to
the mean and standard deviation of noise sample. Hence this study
would try to perform SAECG using consistent root-mean-square
(RMS) noise levels among study subjects and analyze the noise level
effects on SAECG. This study would also evaluate the differences
between normal subjects and chronic renal failure (CRF) patients in
the time-domain SAECG parameters.
The study subjects were composed of 50 normal Taiwanese and 20
CRF patients. During the signal-averaged processing, different RMS
noise levels were adjusted to evaluate their effects on three time
domain parameters (1) filtered total QRS duration (fQRSD), (2) RMS
voltage of the last QRS 40 ms (RMS40), and (3) duration of the low
amplitude signals below 40 μV (LAS40). The study results
demonstrated that the reduction of RMS noise level can increase
fQRSD and LAS40 and decrease the RMS40, and can further increase
the differences of fQRSD and RMS40 between normal subjects and
CRF patients. The SAECG may also become abnormal due to the
reduction of RMS noise level. In conclusion, it is essential to establish
diagnostic criteria of SAECG using consistent RMS noise levels for
the reduction of the noise level effects.
Abstract: The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of
signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we
present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In
addition, this paper will include two main R peak detection methods
by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on
Dyadic Wavelet Transform DyWT.
Abstract: The multi-agent system for processing Bio-signals
will help the medical practitioners to have a standard examination
procedure stored in web server. Web Servers supporting any standard
Search Engine follow all possible combinations of the search
keywords as an input by the user to a Search Engine. As a result, a
huge number of Web-pages are shown in the Web browser. It also
helps the medical practitioner to interact with the expert in the field
his need in order to make a proper judgment in the diagnosis phase
[3].A web server uses a web server plug in to establish and
maintained the medical practitioner to make a fast analysis. If the
user uses the web server client can get a related data requesting their
search. DB agent, EEG / ECG / EMG agents- user placed with
difficult aspects for updating medical information-s in web server.
Abstract: The technological concepts such as wireless hospital
and portable cardiac telemetry system require the development of
physiological signal acquisition devices to be easily integrated into
the hospital database. In this paper we present the low cost, portable
wireless ECG acquisition hardware that transmits ECG signals to a
dedicated computer.The front end of the system obtains and
processes incoming signals, which are then transmitted via a
microcontroller and wireless Bluetooth module. A monitoring
purpose Bluetooth based end user application integrated with patient
database management module is developed for the computers. The
system will act as a continuous event recorder, which can be used to
follow up patients who have been resuscitatedfrom cardiac arrest,
ventricular tachycardia but also for diagnostic purposes for patients
with arrhythmia symptoms. In addition, cardiac information can be
saved into the patient-s database of the hospital.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: The aim of this study was to remove the two principal
noises which disturb the surface electromyography signal
(Diaphragm). These signals are the electrocardiogram ECG artefact
and the power line interference artefact. The algorithm proposed
focuses on a new Lean Mean Square (LMS) Widrow adaptive
structure. These structures require a reference signal that is correlated
with the noise contaminating the signal. The noise references are
then extracted : first with a noise reference mathematically
constructed using two different cosine functions; 50Hz (the
fundamental) function and 150Hz (the first harmonic) function for
the power line interference and second with a matching pursuit
technique combined to an LMS structure for the ECG artefact
estimation. The two removal procedures are attained without the use
of supplementary electrodes. These techniques of filtering are
validated on real records of surface diaphragm electromyography
signal. The performance of the proposed methods was compared with
already conducted research results.
Abstract: Heart-s electric field can be measured anywhere on
the surface of the body (ECG). When individuals touch, one person-s
ECG signal can be registered in other person-s EEG and elsewhere
on his body. Now, the aim of this study was to test the hypothesis
that physical contact (hand-holding) of two persons changes their
heart rate variability. Subjects were sixteen healthy female (age: 20-
26) which divided into eight sets. In each sets, we had two friends
that they passed intimacy test of J.sternberg. ECG of two subjects
(each set) acquired for 5 minutes before hand-holding (as control
group) and 5 minutes during they held their hands (as experimental
group). Then heart rate variability signals were extracted from
subjects' ECG and analyzed in linear feature space (time and
frequency domain) and nonlinear feature space. Considering the
results, we conclude that physical contact (hand-holding of two
friends) increases parasympathetic activity, as indicate by increase
SD1, SD1/SD2, HF and MF power (p
Abstract: The study aimed to evaluated the reproductive performance response to short term oestrus synchronization during the transition period. One hundred and sixty-five indigenous multiparous non-lactating goats were subdivided into the following six treatment groups for oestrus synchronization: NT control Group (N= 30), Fe-21d, FGA vaginal sponge for 21days+eCG at 19thd; FPe- 11d, FGA 11d + PGF2α and eCG at 9th d; FPe-10d, FGA 10d+ PGF2α and eCG at 8th d; FPe-9d, FGA 9d +PGF2α and eCG at 7thd; PFe-5d, PGF2α at d0 + FGA 5d + eCG at 5thd. The goats were natural mated (1 male/6 females). Fecundity rates (n. births /n. females treated x 100) were statistically higher (P < 0.05) in short term FPe-9d (157.9%), FPe- 11d (115.4%), FPe-10d (111.1%) and PFe-5d (107.7%) groups compared to the NT control Group (66.7%).
Abstract: Deaths from cardiovascular diseases have decreased substantially over the past two decades, largely as a result of advances in acute care and cardiac surgery. These developments have produced a growing population of patients who have survived a myocardial infarction. These patients need to be continuously monitored so that the initiation of treatment can be given within the crucial golden hour. The available conventional methods of monitoring mostly perform offline analysis and restrict the mobility of these patients within a hospital or room. Hence the aim of this paper is to design a Portable Cardiac Telemedicine System to aid the patients to regain their independence and return to an active work schedule, there by improving the psychological well being. The portable telemedicine system consists of a Wearable ECG Transmitter (WET) and a slightly modified mobile phone, which has an inbuilt ECG analyzer. The WET is placed on the body of the patient that continuously acquires the ECG signals from the high-risk cardiac patients who can move around anywhere. This WET transmits the ECG to the patient-s Bluetooth enabled mobile phone using blue tooth technology. The ECG analyzer inbuilt in the mobile phone continuously analyzes the heartbeats derived from the received ECG signals. In case of any panic condition, the mobile phone alerts the patients care taker by an SMS and initiates the transmission of a sample ECG signal to the doctor, via the mobile network.
Abstract: Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG's. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna's two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna's method.
Abstract: This paper presents the design, implementation and
results related to the storage system of medical information
associated to the ECG (Electrocardiography) signal. The system
includes the signal acquisition modules, the preprocessing and signal
processing, followed by a module of transmission and reception of
the signal, along with the storage and web display system of the
medical platform. The tests were initially performed with this signal,
with the purpose to include more biosignal under the same system in
the future.
Abstract: In this study, to compress ECG signals, KLT (Karhunen-
Loeve Transform) method has been used. The purpose of this method is to
perform effective ECG coding by a correlation between the length of frames
and the number of vectors of ECG signals.
Abstract: The measurement of anesthetic depth is necessary in
anesthesiology. NN10 is very simple method among the RR intervals
analysis methods. NN10 parameter means the numbers of above the 10
ms intervals of the normal to normal RR intervals.
Bispectrum analysis is defined as 2D FFT. EEG signal reflected the
non-linear peristalsis phenomena according to the change brain
function. After analyzing the bispectrum of the 2 dimension, the most
significant power spectrum density peaks appeared abundantly at the
specific area in awakening and anesthesia state. These points are
utilized to create the new index since many peaks appeared at the
specific area in the frequency coordinate. The measured range of an
index was 0-100. An index is 20-50 at an anesthesia, while the index is
90-60 at the awake.
In this paper, the relation between NN10 parameter using ECG and
bisepctrum index using EEG is observed to estimate the depth of
anesthesia during anesthesia and then we estimated the utility of the
anesthetic.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).