Classification of fetal heart rate tracings based on wavelet-transform and self-organizing-map neural networks

Vasios, George; Prentza, Andriana; Blana, D.; Salamalekis, E.; Thomopoulos, P.; Giannaris, Dimitrios; Koutsouris, D.

doi:10.1109/iembs.2001.1020527

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…They were used mother wavelet Daubechies 20-tap, the decomposition was performed up to scale index 5 and SOM neural network consist of 2layers an input layer and 2 output .Data collected for 35 woman .They concluded that fetal pulse oximetry seems to be an important additional source of information.Not only the time , but also the minimum value that FSpO 2 reaches play an important role in the classification of patterns, especially in the cases of nonreassuring rate patterns. Performance of neural network was the sensitivity=83%,the specificity =96%,and the accuracy =91% [9].…”

Section: Fuzzy System Models Analysis Resultsmentioning

confidence: 99%

“…The fetal HR papers are converted to image file in the computer by using Smart Scan high-resolution scanner. The resulting image file is saved as a JPEG image [6]. The original images that were taken by scanner were have size range (2220*1700) to (3500*2560) pixels and they were reduced by Photoshop to the range (1000*203) to (1000*230) pixels.…”

Section: Conversion Of Fetal Hr Papermentioning

confidence: 99%

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Classification of fetal abnormalities based on CTG signal

Journal¹

2014

Baghdad Sci.J

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The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

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Section: Fuzzy System Models Analysis Resultsmentioning

confidence: 99%

Section: Conversion Of Fetal Hr Papermentioning

confidence: 99%

Classification of fetal abnormalities based on CTG signal

Journal¹

2014

Baghdad Sci.J

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“…In the case of FHR, it proved to be helpful in investigating the long-term non-stationary behavior of FHR [55].…”

Section: Wavelet Transformmentioning

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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“…Mathematical models incorporate wavelet transform (WT), time–frequency approaches, Fourier transform, statistical signal analysis, and higher-order statistics. AI approaches towards signal recognition include artificial neural networks (ANN) [ 23 ], self-organizing map (SOM) neural network [ 24 ], finite impulse response (FIR) neural network [ 25 ], and fuzzy logic system [ 26 ]; a technique combining the adaptive noise canceller and adaptive signal enhancer [ 27 ] in a single recurrent neural network is being used for the processing of AECG signal. Singular value decomposition [ 13 ] and IIR adaptive filtering combined with genetic algorithms [ 28 ] are also used for FHR monitoring.…”

Section: Introductionmentioning

confidence: 99%

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

et al. 2009

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Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system.

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Classification of fetal heart rate tracings based on wavelet-transform and self-organizing-map neural networks

Cited by 10 publications

References 25 publications

Classification of fetal abnormalities based on CTG signal

Classification of fetal abnormalities based on CTG signal

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

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

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