Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems

Raghavendra, B. S.; Bera, Debaditya; Bopardikar, Ajit S.; Narayanan, Rangavittal

doi:10.1109/wowmom.2011.5986196

Cited by 34 publications

(22 citation statements)

References 37 publications

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“…The DTW method measures the similarity between two sequences, which may vary in time or speed, to obtain an optimal match between two given sequences with certain restrictions (Myers et al 1981, Li Q et al 2012). For example, Zifan et al (2006) used the piecewise derivative dynamic time warping (PDDTW) method for automated ECG segmentation, while Raghavendra et al (2011) used DTW for arrhythmia detection in e-Healthcare systems.…”

Section: Ekf25 Approach With Nonlinear Phase Observationmentioning

confidence: 99%

“…The DTW distance between two sequences is computed as follows (Raghavendra et al 2011). Let the two sequences be represented as X = (x 1 , x 2 , · · · , x m ) of length m, and Y = (y 1 , y 2 , · · · , y n ) of length n. The DTW distance D(X, Y ) between X and Y is defined as D(X, Y ) = f (m, n), where:…”

Section: Ekf25 Approach With Nonlinear Phase Observationmentioning

confidence: 99%

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ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Akhbari¹,

Shamsollahi²,

Jutten³

et al. 2016

Physiol. Meas.

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Abstract. In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods.

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Section: Ekf25 Approach With Nonlinear Phase Observationmentioning

confidence: 99%

Section: Ekf25 Approach With Nonlinear Phase Observationmentioning

confidence: 99%

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Akhbari¹,

Shamsollahi²,

Jutten³

et al. 2016

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…However, these works are not practical due to the following reasons. Many works [ 5 , 8 , 18 , 20 , 39 – 42 , 44 – 47 ] use only clean ECGs or ECGs with simple artifacts. In fact, real ECG data are always contaminated by noise and contain various types of ECG artifacts.…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

Sivaraks

Ratanamahatana

2015

Computational and Mathematical Methods in Medicine

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Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

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“…However, this is not an ideal measure because, unless two signals are very similar to begin with, Euclidean interpolation of two action potentials leads to intermediary shapes that do not resemble the shape of a prototypical action potential. Another approach is to use dynamic time warping (DTW) to align two action potentials in time before comparing them with a Euclidean distance [10,11]. However, DTW does not capture variations in the amplitude of the action potential, hence the shape of a warped action potential may still not resemble that of a prototypical one.…”

Section: Fig 1 Sample Action Potential With Common Biological Measumentioning

confidence: 99%

A Metamorphosis Distance for Embryonic Cardiac Action Potential Interpolation and Classification

Gorospe

Younes

Tung

et al. 2013

Advanced Information Systems Engineering

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The use of human embryonic stem cell cardiomyocytes (hESC-CMs) in tissue transplantation and repair has led to major recent advances in cardiac regenerative medicine. However, to avoid potential arrhythmias, it is critical that hESC-CMs used in replacement therapy be electrophysiologically compatible with the adult atrial, ventricular, and nodal phenotypes. The current method for classifying the electrophysiology of hESC-CMs relies mainly on the shape of the cell's action potential (AP), which each expert subjectively decides if it is nodallike, atrial-like or ventricular-like. However, the classification is difficult because the shape of the AP of an hESC-CMs may not coincide with that of a mature cell. In this paper, we propose to use a metamorphosis distance for comparing the AP of an hESC-CMs to that of an adult cell model. This involves constructing a family of APs corresponding to different stages of the maturation process, and measuring the amount of deformation between APs. Experiments show that the proposed distance leads to better interpolation and classification results.

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Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems

Cited by 34 publications

References 37 publications

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

A Metamorphosis Distance for Embryonic Cardiac Action Potential Interpolation and Classification

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