Efficient implementation of LMS adaptive filter‐based FECG extraction on an FPGA

Vasudeva, Bhavya; Deora, Puneesh; Pradhan, Pyari Mohan; Dasgupta, S.

doi:10.1049/htl.2020.0016

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The method is implemented in an OMAP L137 embedded processor for realtime applications. Bhavya Vasudeva et al [11] presented an FPGA-based fetal heart rate monitoring system using an adaptive least mean square filter (LMS-AF) for fECG extraction. Raj, A. et al [12] presented a new GWO-SA algorithm that combines gray wolf optimization with sequence analysis to improve non-invasive fetal ECG extraction from overlapping maternal signals.…”

Section: Introductionmentioning

confidence: 99%

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

Mekhfioui,

Benahmed,

Chebak

et al. 2024

Bioengineering

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This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus’s condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy.

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Section: Introductionmentioning

confidence: 99%

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

Mekhfioui,

Benahmed,

Chebak

et al. 2024

Bioengineering

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“…Several FECG signal extraction methods have been explored, including empirical mode decomposition (EMD) methods [ 6 ], least mean square (LMS) error algorithms [ 7 , 8 ], singular value decomposition (SVD) methods [ 9 ], wavelet transform (WT) methods [ 10 ], independent component analysis (ICA) methods, and in particular fast fixed-point ICA (FastICA) algorithms [ 11 ]. Sarafan et al [ 12 ] proposed a method combining ICA, template subtraction (TS), and extended Kalman filter (EKF) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Hao

Yang

Zhou

et al. 2022

Sensors

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Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

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“…However, smartwatches, smartphones and other similar wearable edge devices are used for the continuous and remote monitoring of the heart beat's activities (Barnova K, 2021). Specifically, machine and deep learning algorithms (Wu S, 2013) are embedded in the edge and IoT devices to achieve the best classification of heart rates in accordance to the FECG signals (Vasudeva B, 2020). Unfortunately, this approach to placing complex methods on wearable devices at the edges does not satisfy both scalability and reliability criteria for inthe-moment wellness tracking.…”

mentioning

confidence: 99%

Fpga Based Hardware Entertained Light Weight Self Attention Deep Learning Framework for Better Classification of Ecg Signals

Selvan,

2024

Preprint

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High Speed Extreme learning networks (HSELN) achieve the two-fold challenges such as (i) reducing the complexity in deep learning algorithms without comprising the diagnosis performance and (ii) sustaining the performance of wearable devices suitable for the continuous monitoring of ECG signals. In order to improve resource constraint factors like lower latency, lower utilization, and energy efficiency, the article also focuses on the deployment of deep learning algorithms utilizing Hardware-Software Co-design techniques. Utilizing a variety of ECG datasets for the extended testing and validating it with the ten-cross validation method. Additionally, the suggested method is implemented in multiple Zynq-SoC families, and variables like utilization, latency, and power are computed and compared to numerous current hardware-centric deep learning architectures. Experimentation demonstrates that the proposed deep learning architecture has shown its excellence in diagnosing the ECG signals and proves to be avital solution for deploying in wearable devices.

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Efficient implementation of LMS adaptive filter‐based FECG extraction on an FPGA

Cited by 6 publications

References 28 publications

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fpga Based Hardware Entertained Light Weight Self Attention Deep Learning Framework for Better Classification of Ecg Signals

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