Autoregressive spectral estimation of fetal breathing movement

Ansourian, M.N.; Dripps, J.H.; Beattie, G.; Boddy, K.

doi:10.1109/10.40814

Cited by 25 publications

(6 citation statements)

References 23 publications

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“…However, the results indicate the feasibility of this method for clinical application. The results also suggest that our technique would provide easy assessments of the fetal biophysical status including J Ultrasound Med 13: [19][20][21][22][23][24][25]1994 more information of fetal behavior with less operator and machine time compared with the conventional fetal monitoring method. Using our method, it may be possible to standardize the fetal movements measurements.…”

Section: Discussionmentioning

confidence: 99%

New method for tracking fetal breathing movements using real-time pulsed Doppler ultrasonographic displacement measurement.

Shinozuka

Yamakoshi

Taketani

1994

Journal of Ultrasound in Medicine

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To develop a clinical device for monitoring FBM with a simplified mechanism, the multichannel pulsed Doppler device was achieved by modifying the ultrasonographic module and probe of a cardiotocograph. A new algorithm of signal processing with a highaccuracy displacement estimation technique pro· duced real-time and continuous displacement calculations. In vivo measurements demonstrated that B asic research on fetal behavior in chronic animal models as well as in human fetuses demonstrated that the mode of fetal behavior reflects fetal condition and neural function. t,3 Bio· physical profile scoring, 4 which uses fetal behavioral parameters, is a valuable method in the management of high-risk pregnancy. In the past, to clinically ob· tain changes in fetal motor activity, we have had to observe the fetus by using B-mode ultrasonography over a long time. Moreover, the quantitative assessment of the movements was difficult. Techniques have been devised to measure fetal movements, especially FBM, in the human fetus, such as A-mode ultrasonography,u B-mode imaging,7·S PLL,9.to and the special pressure transducer.n However, these methods were for research purposes and were not directly suitable for routine clinical use. A few devices are currently available for clinical use to detect fetal movements, such as the Actocardiogram developed by Maeda12 and the fetal movement detector system developed by Lindstrom and coworkers. ll These devices estimate fetal movements through the degree of modulated Doppler shift signals, but they can detect nothing more than the presence of relatively large fetal movements and they cannot quantify the type and the magnitude of movement detected; it is impossible for them to trace very small movements, such as FBM.

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Section: Discussionmentioning

confidence: 99%

New method for tracking fetal breathing movements using real-time pulsed Doppler ultrasonographic displacement measurement.

Shinozuka

Yamakoshi

Taketani

1994

Journal of Ultrasound in Medicine

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“…Already in 1989, Ansourian et al [53] presented a digital analysis using AR power spectral estimation with an optimized Burg algorithm, which showed to provide good modeling of short-time series, potentially useful in the monitoring of fetal breathing movement, and a higher resolution than classical FFT, despite the increased computational complexity. Far later, in 2001, Cazares et al [54] used an AR model of uterine activity (UA) to estimate the power at the contraction frequency.…”

Section: Autoregressive Modelsmentioning

confidence: 99%

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

Ponsiglione

Cosentino

Cesarelli

et al. 2021

Sensors

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The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

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“…Periodic breathing can be defined as a breathing pattern in which three or more respiratory pauses of greater than three seconds duration exist with less than 10 seconds of respiration between pauses (10). The fetus begins to practice breathing movements in the uterus in an irregular pattern; therefore, the periodic breathing may be considered as a normal event (11). However, it has been stated that full-term and especially premature infants may have oscillatory patterns corresponding to periodic breathing patterns, and these patterns might cause apneic episodes (12).…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnea: Detection and Classification of Infant Patterns

Anagün

2006

Wiley Encyclopedia of Biomedical Engineering

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Apnea is defined as a period in which an infant stops breathing. When the apnea period is greater than a critical period, serious damage in the infant's brain may result from lack of oxygen. In order to prevent this serious event, a need exists to find a reliable way to detect apnea and to determine causes of apnea. For that reason, sensors exclusively designed for research purposes, and especially manufactured apnea monitors with built‐in sensor, used at homes and pediatrics intensive care units in hospitals have been employed. As a result of the limitations and weaknesses of commercially available apnea monitors, in recent years, artificial intelligence techniques, especially neural networks have gained attention in terms of finding a reliable and alternative way to analyze respiration signals to determine if apnea exists and, if so, the length of apnea. In this study, an overview of apnea, difficulties in apnea monitoring, and elementary information for data acquisition and neural networks are given; a multi‐layered neural network for apnea recognition is designed; and applicability of the designed neural network trained with a set of respiration patterns obtained from infants who have apneic episodes is discussed in terms of detecting and classifying apnea patterns.

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Autoregressive spectral estimation of fetal breathing movement

Cited by 25 publications

References 23 publications

New method for tracking fetal breathing movements using real-time pulsed Doppler ultrasonographic displacement measurement.

New method for tracking fetal breathing movements using real-time pulsed Doppler ultrasonographic displacement measurement.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

Sleep Apnea: Detection and Classification of Infant Patterns

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