Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Kabelkonfektion - Quo vadis?: 5. und 6. November 2003, Stuttgart

Die Komplexität von Leitungssätzen nimmt stetig zu. Die Evolution des Kabelsatzes von der einfachen Verdrahtung mit Kupferrundkabeln hin zu technologisch anspruchsvollen Bordnetzen mit unterschiedlichsten Übertragungsmedien hält weiter an. So sind z. B. Flex-Wiring Lösungen - vor einigen Jahren noch technologisches Neuland - bereits vielfach im Einsatz; Lichtwellenleiter finden Einsatz in Bussystemen und Beleuchtungsapplikationen. Im Rahmen der diesjährigen Tagung wird ein Blick auf den Entwicklungsstand geworfen und mit Experten aus innovativen Unternehmen aus der Verdrahtungs- und Automobilbranche über aktuelle Themenfelder der Verdrahtungstechnologien diskutiert. Schwerpunkte der diesjährigen Veranstaltung sind die Themenkomplexe Bordnetzstruktur, Fertigungstechnik sowie die Kontaktierungstechnik

Steckverbindungssystem fuer folienisolierte Leiter

WO 200135496 A UPAB: 20010724 NOVELTY - The plug-in connection system has a plug-in unit and a counter plug-in element fitted to the ends of respective flexible conductor foils, each provided with a base body (5) and a cooperating cover (6) and a penetration structure between the latter. The flexible conductor foil is inserted before fitting the cover to the base body, the penetration structure penetrating the foil-insulated conductors for mechanically restraining and electrically contacting the latter after bringing the cover into intimate contact with the base body. USE - The plug-in connection system is used for flexible conductor foils with foil-insulated conductors, e.g. for use in a photographic camera. ADVANTAGE - The conductor foil is securely held upon assembly of the plug-in unit or the counter plug-in element

MID im Multifunktionslenkrad

Der Vortrag beschäftigt sich mit dem Einsatz von MID im Multifunktionslenkrad von der Konzeption bis zu Prototypen

A new approach for the clustering using pairs of prototypes

In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods

Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome

A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine

Analysis of electrical uterine contractile activity for prediction of preterm delivery

This study is aimed at evaluation of the capability to indicate the preterm delivery risk analysing the features extracted from signals of electrical uterine activity. Free access database was used with signals acquired in two groups of pregnant women who delivered at term and preterm. Signal features comprised classical time domain and spectral parameters of contractile activity, as well as the sample entropy. Their mean values were calculated over all contraction episodes detected in each record and their statistical significance for separating the two groups of recordings was provided. Influence of electrodes location, band-pass filter settings and gestation week was investigated. The obtained results showed that a spectral parameter – the median frequency was the most promising indicator of the preterm delivery risk

Efficiency of automated detection of uterine contraction using tocography

Monitoring of uterine contractile activity enables to control the progress of labour. Automated detection of contractions is to be an integral part of the signal analysis implemented in computer aided fetal surveillance system. Evaluation of efficiency of three algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. These algorithms are based generally on analysis of the frequency distribution of signal values. The reference data in form of beginning and end of contraction episodes were obtained from human expert. Obtained results showed high efficiency of the algorithms tested where the best one ensured the sensitivity and positive predictive value equal to 92.2 and 97.2, respectively

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall, F-Score and Rand Index has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy