Atrial flutter and atrial tachycardia detection using Bayesian approach with high resolution time–frequency spectrum from ECG recordings

Lee, Jin-Seok; McManus, David D.; Bourrell, Peter; Sörnmo, Leif; Chon, Ki H.

doi:10.1016/j.bspc.2013.04.002

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…The VFCDM method has shown higher resolution than any other time-frequency spectrum methods, such as smoothed pseudo Wigner-Ville (SPWV), short-time Fourier transform (STFT) and wavelet transform (WT) methods [25,26,27,28]. Considering a sinusoidal signal x ( t ) to be a narrow band oscillation with an instantaneous amplitude A ( t ), a center frequency

f_{0}

, a phase ϕ ( t ), and a direct current component dc ( t ):

x false(t false) = d c false(t false) + A false(t false) \cos false(2 π f_{0} t + φ false(t false) false)

…”

Section: Methodsmentioning

confidence: 99%

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Thap

Chung

Jeong

et al. 2016

Sensors

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In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV1/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV1/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV1/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone.

show abstract

f_{0}

, a phase ϕ ( t ), and a direct current component dc ( t ):

x false(t false) = d c false(t false) + A false(t false) \cos false(2 π f_{0} t + φ false(t false) false)

…”

Section: Methodsmentioning

confidence: 99%

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Thap

Chung

Jeong

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on 12 extracted features, the Bayesian network produced an accuracy rate of 86.25% (Wiggins et al, 2008). Another Bayesian-based method was utilized in analyzing interval ECG signals (Lee, McManus, Bourrell, Sörnmo, & Chon, 2013). A high resolution time-frequency spectral method was developed to process atrial activities.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…A high resolution time-frequency spectral method was developed to process atrial activities. The method produced an accuracy rate of 88% (Lee et al, 2013). In Atoui, Fayn, and Rubel (2010), multiple regression-based methods were used to process ECGs from cardiac patients, while in Iacoviello et al (2007), a linear regression analysis was conducted for ECG monitoring.…”

Section: Statistical Modelsmentioning

confidence: 99%

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

Seera

Lim

Liew

et al. 2015

Expert Systems with Applications

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“…Readers may refer to [16] for more information about particle filters. Lee et al in [17] used a particle filter based framework along with a modified polar EDM in order to extract the atrial signal from ECG for detection of Atrial Flutter (AFL) and Atrial Tachycardia (AT). To overcome the computational complexity of PF, Lin et al in [18] proposed to use a marginalized particle filter (MPF) for ECG denoising and implemented the "triangular" model proposed by SchÖn et al [19].…”

Section: Introductionmentioning

confidence: 99%

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy

Hesar

Mohebbi

2017

IEEE J. Biomed. Health Inform.

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In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed algorithm had the lowest MSEPWRD for all noise types at low input SNRs. Therefore, the morphology and diagnostic information of ECG signals were much better conserved compared with EKF/EKS frameworks, especially in non-Gaussian nonstationary situations.

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Atrial flutter and atrial tachycardia detection using Bayesian approach with high resolution time–frequency spectrum from ECG recordings

Cited by 21 publications

References 28 publications

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy

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