Adaptive noise removal in the ECG using the Block LMS algorithm

Rahman, Mohammad Zia Ur; Ahamed, Shaik Rafi; Reddy, D. V. Rama Koti

doi:10.1109/icastech.2009.5409698

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…Impedance of electrode skin interface [10,[23][24][25][26][27][28][29][30] Skin deformation [23,[31][32][33] Triaxial acceleration [25,26,[33][34][35][36][37][38][39][40][41][42][43]] Others [13,[44][45][46][47][48][49] Therefore, in this study, our research can be separated as three steps:…”

Section: Type Of Reference Signal Sensor Referencesmentioning

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: Type Of Reference Signal Sensor Referencesmentioning

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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“…HE multiply-and-accumulate unit (MAC) implements the equation Y=A×B+C and has a central role in several applications, including image and audio processing [1], [2], convolutional neural networks, and adaptive filtering [3]- [5]. This calls for optimized MAC implementations with reduced power and area.…”

Section: Introductionmentioning

confidence: 99%

Approximate MAC unit using Static Segmentation

Meo,

Saggese,

Strollo

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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In this paper we investigate a novel approximate multiply-and-accumulate (MAC) unit, that computes Y=A×B+C using static segmentation. The proposed architecture uses a unique carry-propagate adder and performs segmentation on the three operands A, B, and C, to reduce hardware cost. The circuit can be configured at design-time by two parameters. The first one controls the segmentation on A and B, while the second one controls the segmentation on C and the adder length. An error compensation technique is also employed, to reduce the approximation error. Error analysis and implementation results in 28nm CMOS for 8-bits multiplier with 20-bits and 24-bits addition are presented. The proposed approximate MACs outperform the state of the art, showing the largest power saving when the mean relative error distance (MRED) is larger than 2×10 −3 and 4×10 -5 for 20 and 24-bits addition, respectively. For MRED of about 6×10 −3 the proposed approximate MAC with 20-bits addition exhibits a power reduction larger than 60% compared to the exact MAC and larger than 27% compared to the state-of-the-art approximate MACs. Application examples to image filtering and template matching show that proposed approximate circuits are good candidates in applications where their error performances are acceptable.

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“…Adaptive filters are widely used in many application fields such as telecommunication, biomedical, acoustic, video and image processing, and can solve problems such as system identification, channel equalization, noise, and echo cancellation [1]. Among the different applications, they reduce the noise in the electrocardiograms due to muscle motion and respiration rate [2,3], or can improve the receiver sensitivity in modern transceivers [4,5]. In [6], an adaptive filter performs a noise cancellation for the detection of radar signals; the works [7][8][9] describe variable step-size techniques for the suppression of the echo in the acoustic systems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Power High-Precision Implementation for Sign–Magnitude DLMS Adaptive Filters

Meo¹,

De²,

Petra³

et al. 2022

Electronics

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This paper investigates the use of approximate fixed-width and static segment multipliers in the design of Delayed Least Mean Square (DLMS) adaptive filters based on the sign–magnitude representation for the error signal. The fixed-width approximation discards part of the partial product matrix and introduces a compensation function for minimizing the approximation error, whereas the static segmented multipliers reduce the bit-width of the multiplicands at runtime. The use of sign–magnitude representation for the error signal reduces the switching activity in the filter learning section, minimizing power dissipation. Simulation results reveal that, by properly sizing the two approaches, a steady state mean square error practically unchanged with respect to the exact DLMS can be achieved. The hardware syntheses in a 28 nm CMOS technology reveal that the static segmented multipliers perform better in the learning section of the filter and are the most efficient approach to reduce area occupation, while the fixed-width multipliers offer the best performances in the finite impulse response section and provide the lowest power dissipation. The investigated adaptive filters overcome the state-of-the-art, exhibiting an area and power reduction with respect to the standard implementation up to −18.0% and −64.1%, respectively, while preserving the learning capabilities.

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Adaptive noise removal in the ECG using the Block LMS algorithm

Cited by 13 publications

References 14 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

Approximate MAC unit using Static Segmentation

A Novel Low-Power High-Precision Implementation for Sign–Magnitude DLMS Adaptive Filters

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