Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method

Mateo, J.; Torres, Ana; García, M. A.; Santos, José Luís

doi:10.1007/s00521-015-1988-7

Cited by 19 publications

(10 citation statements)

References 61 publications

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“…In recent decades, various methods have been presented to improve the detection of heart signal waves, including Pan-Tompkins algorithm [ 7 ], Wavelet Transform, by usage of a constant scale in signal analysis, not considering the characteristics of the signal [ 8 , 9 ], and artificial neural networks, containing of a series of interconnected simple processing units that each connection has a weight. Input layer, one or multiple hidden layers, and output layer constitute a neural network [ 10 , 11 ]. Adaptive filter [ 12 ], called Hilbert-Huang Transform (HHT), is a new technique for extracting features that are nonlinear and nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

Shannon’s Energy Based Algorithm in ECG Signal Processing

Beyramienanlou

Lotfivand

2017

Computational and Mathematical Methods in Medicine

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Physikalisch-Technische Bundesanstalt (PTB) database is electrocardiograms (ECGs) set from healthy volunteers and patients with different heart diseases. PTB is provided for research and teaching purposes by National Metrology Institute of Germany. The analysis method of complex QRS in ECG signals for diagnosis of heart disease is extremely important. In this article, a method on Shannon energy (SE) in order to detect QRS complex in 12 leads of ECG signal is provided. At first, this algorithm computes the Shannon energy (SE) and then makes an envelope of Shannon energy (SE) by using the defined threshold. Then, the signal peaks are determined. The efficiency of the algorithm is tested on 70 cases. Of all 12 standard leads, ECG signals include 840 leads of the PTB Diagnostic ECG Database (PTBDB). The algorithm shows that the Shannon energy (SE) sensitivity is equal to 99.924%, the detection error rate (DER) is equal to 0.155%, Positive Predictivity (+P) is equal to 99.922%, and Classification Accuracy (Acc) is equal to 99.846%.

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Section: Introductionmentioning

confidence: 99%

Shannon’s Energy Based Algorithm in ECG Signal Processing

Beyramienanlou

Lotfivand

2017

Computational and Mathematical Methods in Medicine

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“…EEG records are interfered with by various types of noise, which greatly reduces its usefulness. Artificial Neural Networks (ANNs) are an effective in removing EEG interference [24].…”

Section: Noise Reduction and Removalmentioning

confidence: 99%

Automatic Exam Evaluation based on Brain Computer Interface

Balat¹,

El-Dosuky²,

El-Razek³

et al. 2020

IJCA

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Brain computer interface applications can be used to overcome learning problems, especially student anxiety, lack of focus, and lack of attention. This paper introduces a system based on brain computer interface (BCI) to be used in education to measure intended learning outcomes and measure the impact of noise on the degree of system accuracy. This system works online and is based on recorded brain signal dataset. The system can be considered as a special case of P300 speller accepting only letters from A to D. These are the possible answers to multiplechoice questions MCQ. The teacher makes exams, stores them in an exam database and delivers them to students. Students enroll into the system and record their brain signals. Brain signals go through preprocessing phase in which signals undergo low and high pass filter. Then the signals undergo a subsampling and segmentation. The features obtained are used as inputs to Linear Discriminant Analysis (LDA). Gained accuracy is 91%.

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“…In addition to these traditional denoising methods, there are denoising methods based on neural networks [10,11]. Reference [10] proposed a Schrodinger wave equation based on alternative neural information processing architecture, extracting effective motor imagery features while removing EEG noise.…”

Section: Introductionmentioning

confidence: 99%

Auto-Denoising for EEG Signals Using Generative Adversarial Network

Lam

Ling

2022

Sensors

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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.

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Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method

Cited by 19 publications

References 61 publications

Shannon’s Energy Based Algorithm in ECG Signal Processing

Shannon’s Energy Based Algorithm in ECG Signal Processing

Automatic Exam Evaluation based on Brain Computer Interface

Auto-Denoising for EEG Signals Using Generative Adversarial Network

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