A Linear Multi-Layer Perceptron for Identifying Harmonic Contents of Biomedical Signals

Nguyen, Thien Minh; Wira, Patrice

doi:10.1007/978-3-642-41142-7_27

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…[25], an approach based on a linear MLP to learn and estimate on-line the harmonics from measured signals has been proposed. The linear MLP is able estimate any periodic signal by expressing its output as a sum of harmonic components in the Fourier series.…”

Section: Neural Network Methods For Harmonic Identificationmentioning

confidence: 99%

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

Wira¹,

Nguyen²

2017

JEE

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This main contribution of this work is to propose a new approach based on a structure of MLPs (multi-layer perceptrons) for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that are generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.

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Section: Neural Network Methods For Harmonic Identificationmentioning

confidence: 99%

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

Wira¹,

Nguyen²

2017

JEE

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“…For such approaches, the Fourier transformation can be adopted. Fourier series allows for a periodic expansion of individual elements of physiological signals [45][46][47]. In such approach, individual elements of ECG signal may be determined via a system of the mathematical equations, and consequently expanded into the Fourier series [48,49].…”

Section: Recent Workmentioning

confidence: 99%

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Augustýnek¹,

Kubicek²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

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The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary. This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker's functionality. In this work, we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment. The electrical model of the heart allows signals generation (right atrium, right ventricle) and the monitoring of the stimulation pulses. The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time. The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses. The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode. In addition, the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

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“…Sadhukhan et al [8] proposed a technique based on Fourier coefficient suppression which eliminated noises from the ECG data. Nguyen et al [9] provided a linear Multi-Layer Perceptron (MLP) to estimate the frequency content of ECG recording, and the computing time and the mean square error (MSE) required by the MLP all showed better performance compared with Fast Fourier Transform (FFT). Also, Vaneghi et al [10] presented a comparative approach of six most frequently used methods to ECG feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Improved Fourier series expansion methods for electrocardiography analysis

Lee

Nien

Kuan

et al. 2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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In this work, we propose two Fourier series expansion methods (FSEM), one improved from the original FSEM and another a simplified version of the improved FSEM, to significantly enhance the accuracy of solution along with consuming less computation time and to eliminate the Gibbs phenomenon when dealing with electrocardiography (ECG). Keywords-electrocardiography (ECG); Fourier series expansion method (FSEM); Gibbs phenomenonI.

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A Linear Multi-Layer Perceptron for Identifying Harmonic Contents of Biomedical Signals

Cited by 4 publications

References 12 publications

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Improved Fourier series expansion methods for electrocardiography analysis

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