Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy

Choi, Samjin; Adnane, Mourad; Lee, Gi-Ja; Jang, Hoyoung; Jiang, Zhongwei; Park, Hun-Kuk

doi:10.1016/j.eswa.2009.12.069

Cited by 53 publications

(28 citation statements)

References 25 publications

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“…Individual ICP pulses were first extracted from each 20-min recording using a correlation of ICP with R-wave peaks in the ECG signal [10]. Because this method is dependent only locally on the R-wave peaks, the segmentation is sufficiently accurate and largely invariant to heart-rate variability [11].…”

Section: Methodsmentioning

confidence: 99%

Noise reduction in intracranial pressure signal using causal shape manifolds

Rajagopal

Hamilton

Scalzo

2016

Biomedical Signal Processing and Control

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We present the Iterative/Causal Subspace Tracking framework (I/CST) for reducing noise in continuously monitored quasi-periodic biosignals. Signal reconstruction of the basic segments of the noisy signal (e.g. beats) is achieved by projection to a reduced space on which probabilistic tracking is performed. The attractiveness of the presented method lies in the fact that the subspace, or manifold, is learned by incorporating temporal, morphological, and signal elevation constraints, so that segment samples with similar shapes, and that are close in time and elevation, are also close in the subspace representation. Evaluation of the algorithm’s effectiveness on the intracranial pressure (ICP) signal serves as a practical illustration of how it can operate in clinical conditions on routinely acquired biosignals. The reconstruction accuracy of the system is evaluated on an idealized 20-min ICP recording established from the average ICP of patients monitored for various ICP related conditions. The reconstruction accuracy of the ground truth signal is tested in presence of varying levels of additive white Gaussian noise (AWGN) and Poisson noise processes, and measures significant increases of 758% and 396% in the average signal-to-noise ratio (SNR).

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Section: Methodsmentioning

confidence: 99%

Noise reduction in intracranial pressure signal using causal shape manifolds

Rajagopal

Hamilton

Scalzo

2016

Biomedical Signal Processing and Control

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“…The domain transfer SVM method assumes the kernel function k or K matrix as a linear combination of a set of base kernel function k m , which can be defined as where and with further assumption that, ( 6 ) Therefore, final formulation can be written as: ,…”

Section: B Domain Transfer Svmmentioning

confidence: 99%

“…Most of the research has been related to feature extraction and classification of different arrhythmias. The most common features used are morphological features such as R-R intervals, QRS width, P waves, PR segment and ST segment [4][5][6]. In some techniques, Fourier transform and wavelet transform are used to extract the time and frequency based features of the ECG signals [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Domain adaptation methods for ECG classification

Bazi

Alajlan

Alhichri

et al. 2013

2013 International Conference on Computer Medical Applications (ICCMA)

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The detection and classification of heart arrhythmias using Electrocardiogram signals (ECG) has been an active area of research in the literature. Usually, to assess the effectiveness of a proposed classification method, training and test data are extracted from the same ECG record. However, in real scenarios test data may come from different records. In this case, the classification results may be less accurate due to the statistical shift between these samples. In order to solve this issue, we investigate, in this paper, the capabilities of two domain adaption methods proposed recently in the literature of machine learning. The first is known as domain transfer SVM, whereas the second is the importance weighted kernel logistic regression method. To assess the effectiveness of both methods, the MIT-BIH arrhythmia database is used in the experiments.

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“…In this work author classified that the single ECG beat as normal or ischemic based on supervised neural network. In [14] author introduce the ECG beat segmentation technique and this method is consists of two stages, that are signal processing and ECG detector. In signal processing, noises in the ECG signals are removed using wavelet and low pass filter approaches.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach

Subbiah¹,

Subramanian²

2018

IJET

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In present day, several types of developments are carried toward the medical application. There has been increased improveme nt in the processing of ECG signals. The accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise. Initially denoising the ECG signal using filters and detect the PQRS waveforms. Four filters are carried out to remove the ECG noises that are Median, Gaussian, FIR and Butterworth filt er. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The paper classifies the ECG signal into two classes, Normal and Abnormal. ECG waveform is detected and analyzed using the 48 records of the MIT-BIH arrhythmia database. Denoising results are evaluated using MSE, RMSE, PSNR, NAE and NCC. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP).

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Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy

Cited by 53 publications

References 25 publications

Noise reduction in intracranial pressure signal using causal shape manifolds

Noise reduction in intracranial pressure signal using causal shape manifolds

Domain adaptation methods for ECG classification

Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach

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