Adaptive wavelet network for multiple cardiac arrhythmias recognition

Lin, Chia‐Hung; Du, Yi-Chun; Chen, Tainsong

doi:10.1016/j.eswa.2007.05.008

Cited by 84 publications

(34 citation statements)

References 16 publications

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“…4 In addition, proper delineation of ECG waveforms can help to achieve more accurate results in applications such as pattern recognition or arrhythmia classification. 18,20 Therefore, parameterization and detection of the ECG signal events using a reliable algorithm is the first stage in the computer analysis of the ECG signal. Numerous approaches have yet been developed for the aim of detection of the ECG events including mathematical models, 28 Hilbert transform, and the first derivative, 1,12 second-order derivative, 21 wavelet transform and the filter banks, 9,10,19 soft computing (Neuro-fuzzy, genetic algorithm), 14 Hidden Markov Models (HMM) application, 5 etc.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

et al. 2010

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In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT). Next, a sample to sample sliding window is applied to preprocessed sequence and in each slid, mean value, variance, skewness, and kurtosis of the excerpted segment are superimposed called MHOM. The MHOM metric is then used as decision statistic to detect and delineate ECG events. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.95% and P+ = 99.94% are obtained for the detection of QRS complexes, with the average maximum delineation error of 6.1, 4.1, and 6.5 ms for P-wave, QRS complex, and T-wave, respectively showing marginal improvement of detectiondelineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC, and Premature Atrial Complex-PAC) and average values of Se = 99.97% and P+ = 99.95% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process, reliable robustness against strong noise, artifacts, and probable severe arrhythmia(s) of high resolution holter data can be mentioned as important merits and capabilities of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

et al. 2010

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“…The discrete wavelets which are orthonormal dyadic are associated with scaling functions φ(t). The signal can be convolved with the scaling function to produce approximation coefficients [13]. The discrete wavelet transform (DWT) can be written as Tm,n…”

Section: Waveletmentioning

confidence: 99%

R Peak Detection using Wavelet

Yadav¹,

Grover²

2017

IJCA

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ECG is very crucial and important tool to detect the cardiac problems. It has all the information related to the electrical activities of heart. This also has the information of normal and abnormal activities for the detection of the diseases. So it is essential and important to detect the accurate R-peaks in QRS complex, especially when the results are to be used for clinical applications. Hence in a long-term ECG signal, automatic R-peaks detection is very essential to diagnose cardiac disorders. In this paper we proposed a robust technique to detect R-peak which uses Wavelet Transform. The proposed R Peak detector is consists of a wavelet filter banks, a noise detector with zero-crossing points, multi-scaled product algorithm and soft-threshold algorithm.

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“…The non-linear optimization method, such as gradient descent method, steepest descent method or Newton-Raphson method [15][16][17], is employed to adjust the parameter  and minimize the error with iteration procedures. It is intended to minimize the predicted squared…”

Section:  mentioning

confidence: 99%

“…The PNN-based classifier is developed to perform the classification tasks. The performance of this method is presented and promising results are given for classification applications, such as the straightforward mathematical operation, flexible pattern mechanism, and high tolerance capability [15][16][17]. These algorithms can be easily programmed into the FPGA chip.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

Lin¹,

Lin²

2012

JBiSE

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This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transformation (FDT) is employed to adjoin the fractal features of QRS-complex, including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic patterns, which can produce family functions and enhance features, making clear differences between normal and unhealthy subjects. The probabilistic neural network (PNN) is proposed for recognizing multiple cardiac arrhythmias. Numerical experiments verify the efficiency and higher accuracy with the software simulation in order to formulate the mathematical model logical circuits. FDT results in data self-similarity for the same arrhythmia category, the number of dataset requirement and PNN architecture can be reduced. Its simplified model can be easily embedded in the FPGA chip. The prototype classifier is tested using the MIT-BIH arrhythmia database, and the tests reveal its practicality for monitoring ECG signals.

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Adaptive wavelet network for multiple cardiac arrhythmias recognition

Cited by 84 publications

References 16 publications

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

R Peak Detection using Wavelet

FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

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