A New, Phonocardiography-Based Telemetric Fetal Home Monitoring System

Kovács, Ferenc; Török, Marianna; Horváth, Csaba; Balogh, A; Zsedrovits, Tamás; Nagy, Alexandra; Hosszú, Gábor

doi:10.1089/tmj.2010.0039

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…The fPCG could find its use especially in a long-term monitoring and home monitoring which might lead to a reduction in the number of physical visits to the doctor [34]. In contrary to CTG, additional information…”

Section: Fetal Phonocardiographymentioning

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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Section: Fetal Phonocardiographymentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Barnova,

Martinek,

Vilimkova Kahankova

et al. 2024

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…In addition, it is a completely passive method, where neither the mother nor the fetus is exposed to any type of radiation, as is the case with CTG [10]. Due to its non-invasiveness, simplicity, and low cost, the fPCG method could become an alternative to CTG suitable for long-term measurement and could also become a technique used for remote home monitoring [11,12]. The disadvantage of the method is the sensitivity to a wide range of noise, which needs to be filtered in order to obtain clinically valuable information.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of single-channel signal processing methods in fetal phonocardiography

et al. 2022

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Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

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“…Following emergent of m-health interventions in GDM, the development and evaluation of individual interventions have attracted more attention by the researchers. The previous systematic reviews either have focused on the effectiveness of m-health interventions (4, 16, 27-34) or on a single condition (35)(36)(37)(38). However, minimal evidence has been provided on the healthcare utilization or cost analyses.…”

Section: Introductionmentioning

confidence: 99%