Simulation Of Pathological ECG Signal Using Transform Method

Manju, B R; Akshaya, B.

doi:10.1016/j.procs.2020.04.229

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Over the years, many researchers have developed models to better understand cardiac physiology and its assessment through the ECG, including approaches based on reaction–diffusion, oscillators, and transforms [ 6 , 7 , 8 ]. The models have specific advantages and disadvantages, so it is important to understand their properties before deploying them in specific applications.…”

Section: Introductionmentioning

confidence: 99%

“…Transform-based modeling has also been proposed to study ECG signals. In this approach, each ECG cycle is decomposed into P, Q, R, S, and T waves, each modeled using elementary functions and represented by their corresponding Fourier series [ 8 , 9 ]. Such an approach may perform well on a stable ECG (i.e., signals displaying no variation from one cycle to the next).…”

Section: Introductionmentioning

confidence: 99%

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Model-Driven Analysis of ECG Using Reinforcement Learning

et al. 2023

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Modeling is essential to better understand the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We use reinforcement learning to train a deep neural network to estimate the modeling parameters from an ECG recorded in babies from 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. From the 751,510 PQRST complexes modeled, 82.7% provided a signal-to-noise ratio that was sufficient for further analysis (>5 dB). After correction for multiple tests, 10 of the 24 modeling parameters exhibited statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. These results confirm that this model-driven approach can capture sensitive ECG parameters. Due to its physiological interpretability, this approach can provide a window into latent variables which are important for understanding the heart-beating process and its control by the autonomous nervous system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Driven Analysis of ECG Using Reinforcement Learning

et al. 2023

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“…Over the years, many researchers have developed models to better understand cardiac physiology and its assessment through the ECG, including approaches based on reactiondiffusion, oscillators, and transforms [6][7][8]. The models have specific advantages and disadvantages, so it is important to understand their properties before deploying them in specific applications.…”

Section: Introductionmentioning

confidence: 99%

Model-Driven Analysis of ECG Using Reinforcement Learning

O’Reilly¹,

Oruganti²,

Tilwani³

et al. 2023

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Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

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