scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

A Fuzzy Inference Method-based Fetal Distress Monitoring System

TL;DR: An efficient model to monitor fetal non-reassuring status is proposed by simple operation, and the recognized signals and non- reassuring information are used for judgement.
Abstract: Fetal heart rate (FHR) and uterine pressure (UP) are two of the most important factors for obstetricians to diagnose in antenatal examination. Traditional manual monitoring process is time-consuming and labor-intensive. This paper proposes an efficient model to monitor fetal non-reassuring status. By simple operation, the recognized signals and non- reassuring information are used for judgement. In this system, the FHR and UP baselines are calculated by weighted average, and then heartbeat acceleration, heartbeat deceleration, uterine construction, heartbeat noise pattern, and uterine noise pattern can be easily recognized. The assay of non-reassuring fetal status is achieved by 23 fuzzy rules. When non-reassuring status is found, the alarm mechanism will be triggered for immediate treatment. For verification, a signal simulator is designed and the simulation results are presented for comparisons.
Citations
More filters
Journal ArticleDOI
TL;DR: A two-step analysis of fetal heart rate recordings that allows for effective prediction of the acidemia risk and the obtained results confirm the efficacy of the proposed methods of computerized analysis of FHR signals in the evaluation of the risk of neonatal acidemia.
Abstract: Cardiotocography is the primary method for biophysical assessment of fetal state, which is mainly based on the recording and analysis of fetal heart rate (FHR) signal. Computerized systems for fetal monitoring provide a quantitative analysis of FHR signals, however the effective methods of qualitative assessment that could support the process of medical diagnosis are still needed. The measurements of hydronium ions concentration (pH) in neonatal cord blood are an objective indicator of the fetal outcome. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a two-step analysis of fetal heart rate recordings that allows for effective prediction of the acidemia risk. The first step consists in fuzzy classification of FHR signals. Fuzzy inference corresponds to the clinical interpretation of signals based on the FIGO guidelines. The goal of inference is to eliminate recordings indicating the fetal wellbeing from the further classification process. In the second step, the remained recordings are nonlinearly classified using multilayer perceptron and Lagrangian Support Vector Machines (LSVM). The proposed procedures are evaluated using data collected with computerized fetal surveillance system. The assessment performance is evaluated with the number of correct classifications (CC) and quality index (QI) defined as the geometric mean of sensitivity and specificity. The highest CC=92.0% and QI=88.2% were achieved for the Weighted Fuzzy Scoring System combined with the LSVM algorithm. The obtained results confirm the efficacy of the proposed methods of computerized analysis of FHR signals in the evaluation of the risk of neonatal acidemia.

63 citations

Journal ArticleDOI
TL;DR: The proposed method is tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies–Bouldin index and Xie–Beni index for minimum number ofclasses.
Abstract: Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies---Bouldin index and Xie---Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.

57 citations


Cites methods from "A Fuzzy Inference Method-based Feta..."

  • ...In [45], the signal describing parameters were used as inputs to fetal distress fuzzy monitoring system....

    [...]

Journal ArticleDOI
TL;DR: The clinical interpretation guidelines provided by FIGO were used to develop the weighted fuzzy scoring system for qualitative assessment of the fetal state, and agreement of the fuzzy classification system with the neonatal outcome assessment was analyzed.

33 citations

Journal ArticleDOI
TL;DR: The most significant feature of the proposed method is the high generalization ability being the result of the e-insensitive learning (FCeH clustering), while maintaining the possibility of interpreting the learning outcomes thanks to the linguistic representation of the knowledge in the form of fuzzy conditional (if-then) rules.
Abstract: Objective: In this study we propose a fuzzy classifier whose rule antecedents are determined based on the new approach to Clustering with Pairs of Prototypes (CPP). After demonstrating the high generalization ability of the classifier for six various benchmark datasets, a particular emphasis was placed on the application to support fetal state assessment based on the classification of cardiotocographic (CTG) signals. Methods: The CPP is a solution aimed at increasing the performance of fuzzy classifiers by introducing additional prototypes to those obtained using a given basal clustering method. The CPP improvement was achieved by applying the Fuzzy Clustering with e-Hyperballs (FCeH) as basal clustering, as well as a new ant algorithm-based method of searching for pairs of prototypes. Results: The results were compared with three reference methods: the Lagrangian SVM with the Gaussian kernel function, and the same fuzzy classifier, but using the antecedents determined with the fuzzy c-means and the fuzzy ( c + p ) -means clustering. In case of five out of six benchmark datasets as well as for the CTG signals classification problem we achieved the highest generalization ability measured with the classification accuracy (benchmark data) and the classification quality index defined as geometric mean of sensitivity and specificity (CTG signals). Conclusions: The results of the numerical experiments showed high accuracy of the CPP-based fuzzy classifier when assessing various types of data. Moreover, the two-step classification of the CTG signals based on the proposed method allows for the efficient signal evaluation aiming to support the automated fetal state assessment. Significance and main impact: The most significant feature of the proposed method is the high generalization ability being the result of the e-insensitive learning (FCeH clustering), while maintaining the possibility of interpreting the learning outcomes thanks to the linguistic representation of the knowledge in the form of fuzzy conditional (if-then) rules. Therefore, we believe that this solution will have a positive impact on other studies on intelligent systems.

13 citations

Journal ArticleDOI
TL;DR: It is possible to enhance the process of the fetal condition assessment with classification of the FHR records through the implementation of the heuristic rules of inference in the fuzzy signal processing algorithms.
Abstract: Objectives: Fetal monitoring based on the analysis of the fetal heart rate (FHR) signal is the most common method of biophysical assessment of fetal condition during pregnancy and labor. Visual analysis of FHR signals presents a challenge due to a complex shape of the waveforms. Therefore, computer-aided fetal monitoring systems provide a number of parameters that are the result of the quantitative analysis of the registered signals. These parameters are the basis for a qualitative assessment of the fetal condition. The guidelines for the interpretation of FHR provided by FIGO are commonly used in clinical practice. On their basis a weighted fuzzy scoring system was constructed to assess the FHR tracings using the same criteria as those applied by expert clinicians. The effectiveness of the automated classification was evaluated in relation to the fetal outcome assessed by Apgar score. Material and methods: The proposed automated system for fuzzy classification is an extension of the scoring systems used for qualitative evaluation of the FHR tracings. A single fuzzy rule of the system corresponds to a single evaluation principle of a signal parameter derived from the FIGO guidelines. The inputs of the fuzzy system are the values of quantitative parameters of the FHR signal, whereas the system output, which is calculated in the process of fuzzy inference, defines the interpretation of the FHR tracing. The fuzzy evaluation process is a kind of diagnostic test, giving a negative or a positive result that can be compared with the fetal outcome assessment. The present retrospective study included a set of 2124 one-hour antenatal FHR tracings derived from 333 patients, recorded between 24 and 44 weeks of gestation (mean gestational age: 36 weeks). Various approaches for the research data analysis, depending on the method of interpretation of the individual patient-tracing relation, were used in the investigation. The quality of the fuzzy analysis was defined by the number of correct classifications (CC) and the additional index QI – the geometric mean of the sensitivity and specificity values. Results: The effectiveness of the fetal assessment varied, depending on the assumed relation between a patient and a set of her tracings. The approach, based on a common assessment of the whole set of tracings recorded for a single patient, provided the highest quality of automated classification. The best results (CC = 70.9% and QI = 84.0%) confirmed the possibility of predicting the neonatal outcome using the proposed fuzzy system based on the FIGO guidelines. Conclusions: It is possible to enhance the process of the fetal condition assessment with classification of the FHR records through the implementation of the heuristic rules of inference in the fuzzy signal processing algorithms.

8 citations


Cites methods from "A Fuzzy Inference Method-based Feta..."

  • ...As an extension of these studies, a fuzzy inference system that allows to predict fetal distress on the basis of the analysis of cardiotocographic signals was proposed [8]....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy).
Abstract: Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.

274 citations


"A Fuzzy Inference Method-based Feta..." refers methods in this paper

  • ...Except for the digital wave filter technique was generally used to cope with FHR signal [3-6], Cazares et al....

    [...]

Journal ArticleDOI
TL;DR: A neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms using a fuzzy neural network based on the Hermite characterization of the QRS complexes.
Abstract: This paper presents a neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to a fuzzy neural network for recognition. The results of numerical experiments have confirmed very good performance of such a solution.

175 citations


"A Fuzzy Inference Method-based Feta..." refers methods in this paper

  • ...[2] used neural network to analyze electrocardiograms (ECG's)....

    [...]

Journal ArticleDOI
TL;DR: A real-time method for fetal heart rate monitoring based on signal processing of the fetal heart sounds is presented, and a large number of clinical tests have shown the very good performance of the phonocardiographic method in comparison with FHR curves simultaneously recorded with ultrasound cardiotocography.
Abstract: A real-time method for fetal heart rate (FHR) monitoring based on signal processing of the fetal heart sounds is presented. The acoustic method, which utilizes an adaptive time pattern analysis to select and analyze those heartbeats that can be recorded without artefact, is guided by a number of rules involving an introduced confidence factor on the timing prediction. The algorithm was implemented in a low-power portable electronic instrument to enable long-term fetal surveillance. A large number of clinical tests have shown the very good performance of the phonocardiographic method in comparison with FHR curves simultaneously recorded with ultrasound cardiotocography. Indeed, approximately 90% of the time, the acoustic FHR curve remained inside a /spl plusmn/3 beats/min tolerance limit of the reference ultrasound method. The confidence was typically CF>0.85. The acoustic method exceeded a /spl plusmn/5 beats/min limit relative to the ultrasound method approximately 5% of the time. Finally, no relevant FHR data was measured approximately 5% of the time.

112 citations


"A Fuzzy Inference Method-based Feta..." refers methods in this paper

  • ...Except for the digital wave filter technique was generally used to cope with FHR signal [3-6], Cazares et al....

    [...]

Journal ArticleDOI
TL;DR: An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring.
Abstract: An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a /spl plusmn/5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the time.

109 citations


"A Fuzzy Inference Method-based Feta..." refers methods in this paper

  • ...Except for the digital wave filter technique was generally used to cope with FHR signal [3-6], Cazares et al....

    [...]

Journal ArticleDOI
TL;DR: The author has developed a self-organizing QRS-wave recognition system for electrocardiograms (ECGs) using neural networks and an ART2 (adaptive resonance theory) network was employed.
Abstract: The author has developed a self-organizing QRS-wave recognition system for electrocardiograms (ECGs) using neural networks. An ART2 (adaptive resonance theory) network was employed in this self-organizing neural-network system. The system consists of a preprocessor, an ART2, network, and a recognizer. The preprocessor detects R points in the ECG and divides the ECG into cardiac cycles. A QRS-wave is the part of the ECG that is between a Q point and an S point. The input to the ART2 network is one cardiac cycle from which the ART2 network indicates the approximate locations of both the Q and S points. The recognizer establishes search regions for the Q and S points. Then, it locates the Q and S points in each search region. The system uses this method to recognize a QRS-wave. Then, the ART2 network learns the new QRS-wave pattern from the incoming ECG. The ART2 network self-organizes in response to the input ECG. The average recognition error of the present system is less than 1 ms in the recognition of the Q and S points. >

99 citations