Data censoring with set-membership algorithms

Diniz, Paulo S. R.; Yazdanpanah, Hamed

doi:10.1109/globalsip.2017.8308616

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…It can be used to process ECG signals of the fetus [11], eliminate 50 Hz power line interference [11,12], eliminate electromyographic noise interference [13], and eliminate baseline shifts in ECG signals [12,13]. In addition, it has a low computational burden [14], high reliability, and can handle single-channel ECG signals [15], making it very suitable for longterm early screening applications of AF. To address the issue of MA, it has been previously proposed to sand the skin to remove the stratum corneum [7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm

Zhang,

Zhao,

Guo

2024

Sensors

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Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to −16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm

Zhang,

Zhao,

Guo

2024

Sensors

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“…A practical method for using data redundancy is by examining non-informative data. There are various papers benefiting from data censoring, such as censoring outliers in radar data [8], big data processing [5,30], multi-sensor systems [29], sensor-centric data reduction [15], wireless sensor networks [14], and data selective adaptive filters for sparse systems [7,20,23]. These works explain the benefits of censoring data as compared to using all of the data.…”

Section: Introductionmentioning

confidence: 99%

Data Censoring with Set-Membership Affine Projection Algorithm

Karamali¹,

Zardadi²,

Moradi³

2020

csci

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In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of the mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve the misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

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“…An efficient approach to exploit data redundancy is through censoring non-informative data. Indeed, there are many works in literature benefiting from data censoring, such as censoring outliers in radar data [1], big data processing [2], [3], multi-sensor systems [4], sensor-centric data reduction [5], data censoring for energy-efficient communications [6], wireless sensor networks [7], data selective adaptive filters for sparse systems [8], [9], just to name a few. These works illustrate the advantages of censoring data in comparison with utilizing all data.…”

Section: Introductionmentioning

confidence: 99%

Data Censoring with Set-Membership Affine Projection Algorithm

Moradi¹,

Zardadi²

2018

Preprint

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In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

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Data censoring with set-membership algorithms

Cited by 8 publications

References 16 publications

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm

Data Censoring with Set-Membership Affine Projection Algorithm

Data Censoring with Set-Membership Affine Projection Algorithm

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