45,175 research outputs found
Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine
The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Research into the formation of a soccer curriculum in early childhood education Through the action research with students belonging to IPU’s soccer club and nursery school children
This six-month research examined the influence of playing soccer on the mental and physical development of 5 year old nursery school children under the corporation of students from IPU’s soccer club. Our objectives were to observe: 1) how soccer is a stimulative for young children as a content of early childhood education, 2) what kinds of effects children gain from a developmental point of view. 3) the possibility developing a new area of the early childhood education and care. Our conclusions suggest the following findings: 1)soccer is effective for improving children\u27s health, motor skills and the formation of human relations ability, 2)the most important coaching method for young children is child-oriented[without strong teacher intervention eacherintervention]as children develop their ability in small group, 3)it provides numerous chances to understand each child’s personality more deeply through such activities
Patient Specific Congestive Heart Failure Detection From Raw ECG signal
In this study; in order to diagnose congestive heart failure (CHF) patients,
non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled
frequency and windowed record with different time of ECG records are used. All
of the data rows are labelled with their belongings to classify much more
realistically. SODPs are divided into different radius of quadrant regions and
numbers of the points fall in the quadrants are computed in order to extract
feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis
function, and artificial neural network are used as classifier. The results are
considered in two step validation methods as general k-fold cross-validation
and patient based cross-validation. As a result, it is shown that using neural
network classifier with features obtained from SODP, the constructed system
could distinguish normal and CHF patients with 100% accuracy rate. KeywordsComment: Congestive heart failure, ECG, Second-Order Difference Plot,
classification, patient based cross-validatio
Heart Rate Monitoring During Different Lung Volume Phases Using Seismocardiography
Seismocardiography (SCG) is a non-invasive method that can be used for
cardiac activity monitoring. This paper presents a new electrocardiogram (ECG)
independent approach for estimating heart rate (HR) during low and high lung
volume (LLV and HLV, respectively) phases using SCG signals. In this study,
SCG, ECG, and respiratory flow rate (RFR) signals were measured simultaneously
in 7 healthy subjects. The lung volume information was calculated from the RFR
and was used to group the SCG events into low and high lung-volume groups. LLV
and HLV SCG events were then used to estimate the subjects HR as well as the HR
during LLV and HLV in 3 different postural positions, namely supine, 45 degree
heads-up, and sitting. The performance of the proposed algorithm was tested
against the standard ECG measurements. Results showed that the HR estimations
from the SCG and ECG signals were in a good agreement (bias of 0.08 bpm). All
subjects were found to have a higher HR during HLV (HR) compared
to LLV (HR) at all postural positions. The
HR/HR ratio was 1.110.07, 1.080.05,
1.090.04, and 1.090.04 (meanSD) for supine, 45 degree-first
trial, 45 degree-second trial, and sitting positions, respectively. This heart
rate variability may be due, at least in part, to the well-known respiratory
sinus arrhythmia. HR monitoring from SCG signals might be used in different
clinical applications including wearable cardiac monitoring systems
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