Heartbeat Time Series Classification With Support Vector Machines

Kampouraki, A.; Manis, George; Nikou, Christophoros

doi:10.1109/titb.2008.2003323

Cited by 229 publications

(90 citation statements)

References 35 publications

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“…Poincare plot is the graphical representation of present and next RR interval 21 , if x and y are the present and the next state RR interval series, respectively, then from Eqn. (2), the features SD1 and SD2 can be calculated which represent the standard deviation short term and long term variability, respectively of data point perpendicular to the axis of line-of-identity, i.e., SD1 and SD2 are the standard deviations of x 1 and x 2 respectively.…”

Section: Nonlinear Methodsmentioning

confidence: 99%

“…Initial normalisation was done between 0 and 1 with min-max normalisation procedure to avoid bias caused by unbalanced feature values. In this present study, to obtain good generalisation performance in correct choice of the regularisation parameter C and kernel parameter γ, an extensive search was carried out in the parameter space for the values of C є {2 −4 ,.., 2 15 } and γ є {2 −12 ,.., 2 5 } using 10-fold cross-validation on training data, as C attempts to maximise the margin while keeping low value for training error [20][21][22][23][24][25][27][28][29][30] . Out of 57 normal cases, two data sets were prepared which consisted of 25 cases for training and testing each.…”

Section: ( ) ( ) ( )mentioning

confidence: 99%

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Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

Poddar

Kumar

Sharma

2014

DSJ

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Section: Nonlinear Methodsmentioning

confidence: 99%

Section: ( ) ( ) ( )mentioning

confidence: 99%

Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

Poddar

Kumar

Sharma

2014

DSJ

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“…Yuehui et al [19] proposed new timeseries forecasting model based on flexible neural tree (FNT). M. R. Shafiee-chafi et al [20] presented a novel technique based on fuzzy method for chaotic nonlinear time series and validated the proposed method using Mackey-Glass, Lorenz and biological Heart Rate Variability (HRV) time series. Argyro Kampouraki et al [21] used support vector machines (SVMs) for classification of heartbeat time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Cardiac Arrhythmia using Artificial Neural Network

Kelwade¹,

Salankar²

2015

IJCA

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Heart Rate Variability (HRV) is a non-invasive way of analysis for estimating the autonomic nervous system and based on measurement of the variability of heart rate signals. HRV signal is mostly noted for cardiac arrhythmia detection and classification. In this paper, artificial neural network (ANN) is used as a classifier to predict cardiac arrhythmias into five classes. The HRV data, RR interval time series is obtained using the Electrocardiogram (ECG) data from the MIT-BIH Arrhythmia Database. The linear and nonlinear parameters are extracted from RR intervals are used to train Multi-Layer Perceptron (MLP) neural network. In our study of neural network for time series i.e. RR interval time series, the prediction of Normal Sinus Rhythm (NSR), Premature Ventricular Contraction (PVC), Atrial fibrillation (AFIB), Left bundle branch block (LBBB) and Second degree block (BII) can be done using proposed algorithm. The 70% of the datasets are used to train MLP neural network. The proposed algorithm is predicted with next 30% of the datasets and satisfactory results obtained with prediction overall accuracy of 97%.

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“…Time domain parameters are obtained using the RRIntervals [3] [4] [11]. The associated formulae with the frequency and time domain parameters are given in Table 1 as follows.…”

Section: Using Rr-intervalsmentioning

confidence: 99%

Heart Rate Variability Analysis and Pathological Detection

Patial¹,

K²

2013

IJCA

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In order to measure the mortality in the patients suffering from the heart disease we use the term HRV that i.e. Heart Rate Variability. Estimation methods as Parametric and Non-Parametric are used in the analysis of Heart Rate Variability but Heart Rate Variability requires the specific capabilities which are not provided by either of these. The term EMD i.e. Empirical Mode Decomposition adaptively estimates the IMF i.e. Intrinsic Mode Function of the nonlinear and nonstationary signal. The IMF obtained from the EMD is used for the analyses of the HRV latencies of Healthy subjects and of Congestive Heart Failure subjects. In this paper we have considered the 15 Congestive Heart Failure patients, 20 healthy young control patients and 20 healthy old control patients. After finding the IMF from EMD we have calculated the average periods, absolute power, normalised power and cumulative power and concerned plots are drawn for the comparison of the considered subjects. The results obtained shows that the HRV of healthy subjects rises rapidly to its maximum response as compared to the HRV of the pathological subjects. This fact can be used as a promising approach in clinical practise for the screening of specific risk group. General TermsHRV Analysis using EMD, Pathological Detection.

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Heartbeat Time Series Classification With Support Vector Machines

Cited by 229 publications

References 35 publications

Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

Prediction of Cardiac Arrhythmia using Artificial Neural Network

Heart Rate Variability Analysis and Pathological Detection

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