An efficient method for fetal ECG extraction from single channel abdominal ECG

Panigrahy, Damodar; Rakshit, Manas; Sahu, Prasanna Kumar

doi:10.1109/iic.2015.7150908

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Furthermore, results in skin irritation and severe discomfort to pregnant women due to gel used in setting up the electrodes to establish effective electrode configuration. [91,92] In terms of detection accuracy of FHR, it was noticed from Table 1 that 97.47% was achieved with a four-electrode configuration through a three-step method, including time frequency analysis, complex wavelet, and Heuristic algorithm, at a sampling frequency of 300 Hz, resolution of 12 bits, with a gain of 7800. An accuracy of 91% was obtained with 8-electrode and 32-electrode configurations at a sampling frequency of 1 KHz.…”

Section: No Of Electrodes Discussion Referencementioning

confidence: 99%

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

Aggarwal

Wei

2021

Signals

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During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area.

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Section: No Of Electrodes Discussion Referencementioning

confidence: 99%

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

Aggarwal

Wei

2021

Signals

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“…Optimizing an adaptive algorithm using the PSO method is also a suitable tool as mentioned by Anoop et al [76]. According to Panigrahy et al [128], another option is to use PSO to initialize and optimize EKS parameters which participate on the modelling of the mECG T signal according to the morphology of mECG A in aECG. The combination of PSO and EKS yielded ACC = 89.7%, surpassing the basic EKS and adaptive RLS method.…”

Section: Nature-inspired Optimization Techniques In Noise Suppressionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Barnova,

Martinek,

Vilimkova Kahankova

et al. 2024

Arch Computat Methods Eng

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Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.

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“…On the other hand, fewer algorithms depend on the singlelead aECG signal; e.g., template subtraction (TS) [13,[23][24][25][26], and its variation based on singular value decomposition (SVD) or principal component analysis [27,28], the time-frequency analysis, like wavelet transform, pseudo-smooth Wigner-Ville distribution [29][30][31][32] (in practice, three aECG channels are averaged in [30]), and S-transform [33], sequential total variation [34], adaptive neuro-fuzzy inference system and extended Kalman filter [35], particle swarm optimization and extended Kalman smoother [36] state space reconstruction via lag map [37,38], etc. We refer the reader to, e.g., Sameni and Clifford [4] and Andreotti et al [39] for a more detailed review.…”

Section: Introductionmentioning

confidence: 99%

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

2017

Front. Appl. Math. Stat.

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The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we need to solve both challenges. We propose a novel method to extract the fetal ECG from a single-lead maternal abdominal ECG, without any additional measurement. The algorithm is composed of three components. First, the maternal and fetal heart rates are estimated by the de-shape short time Fourier transform (STFT), which is a recently proposed nonlinear time-frequency analysis technique. The beat tracking technique is the second component which is applied to accurately obtain the maternal and fetal R peaks. The third component consists of establishing the maternal and fetal ECG waveforms by the nonlocal median. The algorithm is tested on two real databases with the annotation provided by experts (adfecgdb database and CinC2013 database) and a simulated database (fecgsym), and provides the state-of-the-art results. We conclude that with the proposed algorithm, the fetal ECG waveform and the fetal heart rate could be accurately obtained from the single-lead maternal abdominal ECG.

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An efficient method for fetal ECG extraction from single channel abdominal ECG

Cited by 10 publications

References 20 publications

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

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