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 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: 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: 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: 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: 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: 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).
Abstract: Electrocardiogram (ECG) data compression algorithm
is needed that will reduce the amount of data to be transmitted, stored
and analyzed, but without losing the clinical information content. A
wavelet ECG data codec based on the Set Partitioning In Hierarchical
Trees (SPIHT) compression algorithm is proposed in this paper. The
SPIHT algorithm has achieved notable success in still image coding.
We modified the algorithm for the one-dimensional (1-D) case and
applied it to compression of ECG data.
By this compression method, small percent root mean square
difference (PRD) and high compression ratio with low
implementation complexity are achieved. Experiments on selected
records from the MIT-BIH arrhythmia database revealed that the
proposed codec is significantly more efficient in compression and in
computation than previously proposed ECG compression schemes.
Compression ratios of up to 48:1 for ECG signals lead to acceptable
results for visual inspection.
Abstract: ECG analysis method was developed using ROC
analysis of PVC detecting algorithm. ECG signal of MIT-BIH
arrhythmia database was analyzed by MATLAB. First of all, the
baseline was removed by median filter to preprocess the ECG signal.
R peaks were detected for ECG analysis method, and normal VCG
was extracted for VCG analysis method. Four PVC detecting
algorithm was analyzed by ROC curve, which parameters are
maximum amplitude of QRS complex, width of QRS complex, r-r
interval and geometric mean of VCG. To set cut-off value of
parameters, ROC curve was estimated by true-positive rate
(sensitivity) and false-positive rate. sensitivity and false negative rate
(specificity) of ROC curve calculated, and ECG was analyzed using
cut-off value which was estimated from ROC curve. As a result, PVC
detecting algorithm of VCG geometric mean have high availability,
and PVC could be detected more accurately with amplitude and width
of QRS complex.
Abstract: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
Abstract: In this paper, we propose a method to reduce the
various kinds of noise while gathering and recording the
electrocardiogram (ECG) signal. Because of the defects of former
method in the noise elimination of ECG signal, we use translation
invariant (TI) multiwavelet denoising method to the noise elimination.
The advantage of the proposed method is that it may not only remain
the geometrical characteristics of the original ECG signal and keep the
amplitudes of various ECG waveforms efficiently, but also suppress
impulsive noise to some extent. The simulation results indicate that the
proposed method are better than former removing noise method in
aspects of remaining geometrical characteristics of ECG signal and the
signal-to-noise ratio (SNR).
Abstract: Heart failure is the most common reason of death
nowadays, but if the medical help is given directly, the patient-s life
may be saved in many cases. Numerous heart diseases can be
detected by means of analyzing electrocardiograms (ECG). Artificial
Neural Networks (ANN) are computer-based expert systems that
have proved to be useful in pattern recognition tasks. ANN can be
used in different phases of the decision-making process, from
classification to diagnostic procedures. This work concentrates on a
review followed by a novel method.
The purpose of the review is to assess the evidence of healthcare
benefits involving the application of artificial neural networks to the
clinical functions of diagnosis, prognosis and survival analysis, in
ECG signals. The developed method is based on a compound neural
network (CNN), to classify ECGs as normal or carrying an
AtrioVentricular heart Block (AVB). This method uses three
different feed forward multilayer neural networks. A single output
unit encodes the probability of AVB occurrences. A value between 0
and 0.1 is the desired output for a normal ECG; a value between 0.1
and 1 would infer an occurrence of an AVB. The results show that
this compound network has a good performance in detecting AVBs,
with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy
value is 87.9%.
Abstract: The aim of this paper is to present a new method
which can be used for progressive transmission of electrocardiogram
(ECG). The idea consists in transforming any ECG signal to an
image, containing one beat in each row. In the first step, the beats are
synchronized in order to reduce the high frequencies due to inter-beat
transitions. The obtained image is then transformed using a discrete
version of Radon Transform (DRT). Hence, transmitting the ECG,
leads to transmit the most significant energy of the transformed
image in Radon domain. For decoding purpose, the receptor needs to
use the inverse Radon Transform as well as the two synchronization
frames.
The presented protocol can be adapted for lossy to lossless
compression systems. In lossy mode we show that the compression
ratio can be multiplied by an average factor of 2 for an acceptable
quality of reconstructed signal. These results have been obtained on
real signals from MIT database.
Abstract: Electrocardiogram (ECG) is considered to be the
backbone of cardiology. ECG is composed of P, QRS & T waves and
information related to cardiac diseases can be extracted from the
intervals and amplitudes of these waves. The first step in extracting
ECG features starts from the accurate detection of R peaks in the
QRS complex. We have developed a robust R wave detector using
wavelets. The wavelets used for detection are Daubechies and
Symmetric. The method does not require any preprocessing therefore,
only needs the ECG correct recordings while implementing the
detection. The database has been collected from MIT-BIH arrhythmia
database and the signals from Lead-II have been analyzed. MatLab
7.0 has been used to develop the algorithm. The ECG signal under
test has been decomposed to the required level using the selected
wavelet and the selection of detail coefficient d4 has been done based
on energy, frequency and cross-correlation analysis of decomposition
structure of ECG signal. The robustness of the method is apparent
from the obtained results.