Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

Kutlu, Yakup; Kuntalp, Damla

doi:10.1016/j.cmpb.2011.10.002

Cited by 204 publications

(104 citation statements)

References 34 publications

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“…In addition, no increase in detection was observed once more than 4 levels were selected. Features 1 -4 have been successfully applied in (Subasi & Gürsoy, 2010), while features 5-6 may help in extracting nonlinear behavior from EEG signal (Kutlu & Kuntalp, 2012).…”

Section: Mspca De-noisingmentioning

confidence: 99%

“…Hence, WPD delivers better frequency resolution for the signal being decomposed. Another benefit of the WPD represents the reconstruction of the original signal by combining various decomposition levels (Kutlu & Kuntalp, 2012). For k levels, size of different set of wavelet coefficient (or sub-bands) in WPD will be 2 k , while in DWT it will be k + 1.…”

Section: Mspca De-noisingmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System

Kevrić

Subaşı

2015

scjournal

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Section: Mspca De-noisingmentioning

confidence: 99%

Section: Mspca De-noisingmentioning

confidence: 99%

The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System

Kevrić

Subaşı

2015

scjournal

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“…A feature vector is used for prediction of SCD occurrence using a support vector machine (SVM) and the K-nearest neighbor (KNN) method. To get the best performance, different numbers of nearest neighbors and kernel functions (RBF and polynomial) are tested in the KNN and SVM classifiers [35]. We could reach 5 min of prediction before SCD occurrence and this is a tipping point in this field.…”

Section: Introductionmentioning

confidence: 99%

Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

Houshyarifar¹,

Amirani²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper we present a method to predict sudden cardiac death (SCD) based on the heart rate variability (HRV) signal and recurrence plots and Poincaré plot-extracted features. This work is a challenge since it is aimed to devise a method to predict SCD 5 min before its onset. The method consists of four steps: preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram signal and then the HRV signal is extracted. In the second step, the recurrence plot of the HRV signal and Poincaré plot-extracted features are obtained. Four features from the recurrence plot and three features from the Poincaré plot are extracted. The features are recurrence rate, determinism, entropy and averaged diagonal line length, and SD1, SD2, and SD1/SD2. In the next step, these features are reduced to one feature by the linear discriminant analysis technique.Finally, K-nearest neighbor and support vector machine-based classifiers are used to classify the HRV signals. We use two databases, the MIT/BIH Sudden Cardiac Death Database and PhysioBank Normal Sinus Rhythm Database. We manage to predict SCD occurrence 5 min before the SCD with accuracy of over 92%.

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“…Feature extraction is one of the most important steps in classification and can capture a certain underlying property of ECG [6]. Various kinds of comprehensive features have been extracted to describe ECG; these features can be divided into three categories, including temporal, morphological, and statistical features [7].…”

Section: Introductionmentioning

confidence: 99%

Fast multi-scale feature fusion for ECG heartbeat classification

Yang

Wang

et al. 2015

EURASIP J. Adv. Signal Process.

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Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

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Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

Cited by 204 publications

References 34 publications

The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System

The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System

Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

Fast multi-scale feature fusion for ECG heartbeat classification

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