[PDF] Top 20 HEART RATE SIGNAL CLASSIFICATION BY SMO ALGORİTHM
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HEART RATE SIGNAL CLASSIFICATION BY SMO ALGORİTHM
... Heart Rate Variability (HRV) analysis is based on measuring the variability of heart rate signals and more specifically, the variability in intervals between R peaks of the electrocardiogram ... See full document
5
A SVM adaptive approach for Ventricular disea...
... ECG signal under entropy value ...ECG signal so that QRS detection over the signal will be ...EEG signal. Authors analyze the signal complexities to analyze the signal and ... See full document
5
Rhythm Disorders – Heart Beat Classification of an Elec trocardiogram Signal
... in heart diseases pro- ...accu- rate heart beat classifier which takes the ECG signal as an input and classify it into different rhythm ... See full document
7
The activity of the diaphragm during pulmonary transition in preterm infants : a feasibility and physiological study
... dEMG signal was implemented. In this signal, single breaths could be distinguished as a sinusoidal function ...dEMG signal was smoothed, with a moving average of 50 samples, to eliminate high ... See full document
59
Baseline Assisted Classification of Heart Rate Variability
... the classification accuracy and classification AUC accuracy of almost all classifiers from HRV measures, and tracking of activity can be achieved by measuring the HRV ... See full document
105
Performance Analysis of Classification of Cardiotocograms Using Support Vector Machine based Classifier
... Fetal heart rate (FHR) and uterine contractions (UC) are simultaneously recorded by Cardiotocography ...several signal processing and computer programming based techniques for interpreting a typical ... See full document
8
Localization and classification of heart beats in phonocardiography signals —a comprehensive review
... recognition rate; CVD: Cardiovascular diseases; DCT: Discrete cosine transform; DTW: Dynamic time warping; DWT: Discrete wavelet transform; ECG: Electrocardiogram; EEMD: Ensemble empirical mode decomposition; EMD: ... See full document
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HEART ARRHYTHMIA DETECTION AND CLASSIFICATION FROM ECG SIGNAL USING ARTIFICIAL NEURAL NETWORKS
... multi-class classification, a multi-layer feedforward neural network was employed and trained using backpropagation to minimise the cost ...learning rate, hidden layer size and regularization parameters ... See full document
6
Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications
... nowadays, heart disease is one of the major diseases that cause ...to heart-related diseases has been investigated by many ...death rate can be further brought down if we can predict or identify the ... See full document
6
Area asymmetry of heart rate variability signal
... In a third case study, we were still testing the performance on short-term heartbeat interval series but this time the actual lengths of the series were different. Instead, they were extracted from ECG data of the same ... See full document
14
Classification of Cardiotocography Data with WEKA
... Fetal Heart Rate (FHR) and Uterine Contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before ...several signal ... See full document
7
Feature extraction and classification of heart sound using 1D convolutional neural networks
... of heart sound sig- nals and improve the signal-to-noise ratio ...of heart disease. Gerbarg et al. [13] divided heart sounds into ...of heart sounds by calculating the average ...of ... See full document
11
FPGA IMPLEMENTATION OF DWT FOR ECG SIGNAL PRE-PROCESSING
... the heart electrical ...the heart amid a cardiovascular cycle (R-R interim); these waves are called P, Q, R, S and T; the Q, R, and S waves are dealt with as a solitary composite wave known as the QRS ... See full document
5
A novel technique for fetal heart rate estimation from Doppler ultrasound signal
... The results show that autocorrelation technique allows us to obtain reliable parameters describing the beat-to-beat FHR variability, but only when the length of the applied window stays close to two periods of a ... See full document
17
ECG Signal Analysis: Different Approaches
... the heart is controlled by the opening and closing of valves and as directed by each electrical signal which begins in a group of cells called the (SA) node [4] located in the upper right chamber of the ... See full document
5
Stochastic dynamics of the cardiovascular system
... the heart frequency by the respiration, and clear evidence of the corresponding combinational frequencies in the Fourier spectra for the subject in coma, suggest unidirectional parametric coupling, ... See full document
15
Cardiovascular Disease Prediction using Machine Learning Techniques
... In this study, we used a Cleveland clinic dataset which contains 9 attributes such as age, gender, resting blood pressure(in mm Hg on admission to the hospital), serum cholesterol in mg/dl, fasting blood sugar, Rest ECG ... See full document
9
The Accuracy of Heart Rate-Based Zone Training using Predicted versus Measured Maximal Heart Rate
... Introduction: Heart rate (HR) based zone training has become an increasingly popular method of exercise ...maximal heart rate (PMHR) versus measured maximal heart rates ... See full document
8
Correlation between Heart Rate, Estimated Heart Rate, and Rating of Perceived Exertion during Aerobic Exercise
... training have certain intensities, at which, results are best achieved. Whether it is warming up, weight loss, aerobic training or endurance training, each happen within different percentages of maximum heart ... See full document
9
INFLUENCE OF THE RESPIRATORY SIGNAL IN HEART RATE VARIABILITY ANALYSIS IN THE RESPIRATORY PATTERN IN HEALTHY ELDERLY AND WITH COPD
... Descriptive statistical analyses of the data were expressed as mean ± standard deviation. The Kolmogorov-Smirnov test confirmed the normality of distributions. The between groups difference in anthropometric ... See full document
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