Classification of fetal abnormalities based on CTG signal

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

Computational intelligence methods for predicting fetal outcomes from heart rate patterns

In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different categories suggested by the NIH and commonly being used in practice in hospitals across the United States. In addition, the algorithms developed here were used to predict cord pH levels, the actual condition that the three NIH categories are used to attempt to measure. This thesis demonstrates the importance of machine learning in Maternal and Fetal Medicine. It provides assistance for the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the Pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology to achieve a more accurate prediction of fetal outcomes using Fetal Heartrate and Uterine Activity with accuracies of greater than 99.5% for predicting categories and greater than 70% for fetal acidosis based on pH values --Abstract, page iii

Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

Short term fetal heart rate variation in intrauterine growth restriction

Cardiotocography (CTG), the continuous and simultaneous recording of the fetal heart rate (FHR) and the maternal contractions, is a method widely used for the assessment of fetal well-being, predominantly in pregnancies with increased risk of complications. The Oxford system, developed by Dawes and Redman and implemented in the Sonicaid Fetalcare monitor, provides a computerised analysis of the CTG (cCTG) by taking into consideration a number of numerical, computer based parameters, with Short Term Variation (STV), a measure of the micro fluctuations of the FHR, being one of the most significant ones, especially in the monitoring of fetuses with Intrauterine Growth Restriction (IUGR). The Dawes-Redman algorithm calculates the STV by dividing each minute into 16 segments, each one being 3,75 seconds long and including 7-10 fetal heartbeats, or 6-9 pulse intervals (STV16). The average pulse interval in each section is calculated and the STV16 derives from the difference of the average pulse intervals between two sections. This calculated STV16 does not, however, equal the beat-to-beat variation of the FHR. A series of important studies has demonstrated that, when monitoring fetuses with preterm IUGR, STV16 values under 3ms correlate positively with the development of metabolic acidemia and should prompt to delivery. Theoretically, measurement of the pulse interval in much smaller time fractions, so that every heartbeat would be taken into consideration (instead of one every 7-10 heartbeats), would lead to a more accurate approximation of the beat-to-beat variation with significant advantages for the antenatal monitoring of the fetus. The IntelliSpace Perinatal by Philips Medical, which measures the STV by dividing each minute into 240 segments (STV240), attempts to better approximate the beat-to-beat variation of the FHR. An effort in our department to implement the existing cut-off values of the STV16 as reference values for the new STV240 algorithm has resulted in highly abnormal findings, with STV240 values significantly below the cut-off values of the STV16. This observation led to the hypothesis, that the reference values for the STV240 should be different, and, more precisely, lower in comparison to the existing reference values for the STV16. This hypothesis was not only based on clinical observation. The discrepancy noted between the two different algorithms is also logically sound, as it is to be expected that the variation between two subsequent beats will be notably lower as the variation between 7-10 subsequent heartbeats. We therefore conducted a single-center, non-interventional, prospective clinical study in order to develop clinically relevant reference values for the STV240 and to compare the reference values for the STV240 to the ones for the STV16. At the same time, we studied the effects of RDS prophylaxis on STV240 and STV16, in order to verify if the known transient effects of corticosteroids on the STV could also be detected with the new algorithm for the STV240. A total of 228 CTG traces from 94 patients (86 singleton and 8 twin pregnancies) were registered and included in the final statistical analysis for the development of the reference values. The values of the STV240 were significantly lower in comparison to the ones of the STV16. Moreover, not only the mean values but 95% of the values for the STV240 lay beneath the existent cut-off value for the STV16. The STV240 has a relative strong, statistically significant correlation with the STV16 (r=0,646, p<0,001). A medium, although statistically significant correlation (r=0,373, p<0,001) between week of pregnancy and STV240 was documented, whereas the correlation between STV16 and week of pregnancy was negligible. A transient increase of both the STV240 and STV16 was documented in the first 24h after the first intramuscular corticosteroid administration, when compared to the STV240 and STV16 without RDS prophylaxis or at least 72h after. This was followed by a transient decrease of both the STV240 and STV16 between 24h and 72h after the first intramuscular corticosteroid injection. Our results confirmed our hypothesis and allowed us to calculate the reference values for the STV240. Of paramount importance for every clinician using the new algorithm in her or his everyday practice, is to know that the normal values for the STV240 (not only the mean value but also the 95th percentile) lie beneath the, up until now, established cut-off value for the STV16. This stresses the fact that every clinician using cCTG should be, in advance, well aware of the algorithm implemented in his cCTG monitors. Otherwise, there is the threat of unnecessary iatrogenic premature deliveries, with all relevant risks.Cardiotocographie (CTG), die kontinuierliche und gleichzeitige Aufzeichnung der fetalen Herzfrequenz (FHF) und der mütterlichen Kontraktionen, ist eine Methode, die weithin für die Beurteilung des fetalen Wohlbefindens verwendet wird, vorwiegend bei Schwangerschaften mit erhöhtem Komplikationsrisiko. Das von Dawes und Redman entwickelte Oxford-System, welches im Sonicaid Fetalcare Monitor implementiert ist, bietet eine computerisierte Analyse des CTG (cCTG) unter Berücksichtigung einer Reihe von numerischen, computerbasierten Parametern an. Kurzzeitvariation (KZV), eine Maßnahme der Mikrofluktuationen des FHF, ist einer der bedeutendsten computerbasierten Parameter, vor allem bei der Überwachung von Feten mit intrauteriner Wachstumsrestriktion (IUGR). Der Dawes-Redman-Algorithmus berechnet die KZV, indem er jede Minute in 16 Segmente unterteilt, wobei jedes 3,75 Sekunden lang ist und 7-10 fetale Herzschläge oder 6-9 Pulsintervalle (KZV16) enthält. Das mittlere Pulsintervall in jedem Abschnitt wird berechnet und die KZV16 ergibt sich aus der Differenz der mittleren Pulsintervalle zwischen zwei Abschnitten. Diese berechnete KZV16 entspricht jedoch nicht der beat-to-beat-Variation der FHF. Eine Reihe wichtiger Studien hat gezeigt, dass bei der Überwachung von Feten mit früher IUGR KZV-Werte unter 3ms positiv mit der Entwicklung einer metabolischen Azidämie korrelieren und zur Entbindung führen sollten. Theoretisch würde die Messung des Pulsintervalls in viel kleineren Zeitabschnitten, so dass jeder Herzschlag berücksichtigt wäre (statt eines alle 7-10 Herzschläge), zu einer genaueren Annäherung der beat-to-beat-Variation der FHF führen, mit deutlichen Vorteilen für die antepartale Überwachung des Fetus. Das IntelliSpace Perinatal von Philips Medical, das die KZV auswertet, indem es jede Minute in 240 Segmente teilt (KZV240), versucht die beat-to-beat Variation der FHF besser anzunähern. Ein Versuch, in unserer Abteilung, die vorhandenen cut-off-Werte der KZV16 als Referenzwerte für den neuen KZV240-Algorithmus zu implementieren, hat zu sehr auffälligen Befunden geführt, wobei die KZV240-Werte deutlich unter den cut-off- Werten der KZV16 lagen. Diese Beobachtung führte zu der Hypothese, dass die Referenzwerte für die KZV240 im Vergleich zu den vorhandenen Referenzwerten für die KZV16 niedriger sein sollten. Diese Hypothese beruht nicht nur auf der klinischen Beobachtung. Die zwischen den beiden verschiedenen Algorithmen bemerkte Diskrepanz ist auch theoretisch zu erwarten, weil die Variation zwischen zwei nachfolgenden Herzschlägen deutlich geringer als die Variation zwischen 7-10 nachfolgenden Herzschlägen ist. Wir haben daher in unserer Klinik eine nicht interventionelle, prospektive klinische Studie durchgeführt, um klinisch relevante Referenzwerte für die KZV240 zu entwickeln und diese mit denen für die KZV16 zu vergleichen. Gleichzeitig haben wir die Effekte der RDS-Prophylaxe auf KZV240 und KZV16 untersucht, um zu prüfen, ob die bekannten transienten Effekte von Kortikosteroiden auf der KZV auch mit dem neuen Algorithmus für die KZV240 nachgewiesen werden können. Insgesamt wurden 228 CTGs von 94 Patientinnen (86 Einlings- und 8 Zwillings- Schwangerschaften) registriert und in die endgültige statistische Analyse zur Entwicklung der Referenzwerte einbezogen. Die Werte der KZV240 waren im Vergleich zu der KZV16 deutlich niedriger. Darüber hinaus lagen nicht nur die Mittelwerte, sondern 95% der Werte für die KZV240 unter dem vorhandenen cut-off-Wert für die KZV16. Die KZV240 hat eine relativ starke, statistisch signifikante Korrelation mit der KZV16 (r = 0,646, p <0,001). Eine mittlere, obwohl statistisch signifikante Korrelation (r = 0,373, p <0,001) zwischen Schwangerschaftswoche (SSW) und KZV240 wurde dokumentiert, während die Korrelation zwischen KZV16 und SSW vernachlässig war. In den ersten 24h nach der ersten intramuskulären Kortikosteroidgabe wurde eine vorübergehende Zunahme sowohl der KZV240 als auch der KZV16 dokumentiert, im Vergleich zu den KZV240 und KZV16 ohne RDS-Prophylaxe oder mindestens 72h danach. Darauf folgte eine vorübergehende Abnahme sowohl der KZV240 als auch der KZV16 zwischen 24h und 72h nach der ersten intramuskulären Kortikosteroidgabe. Unsere Ergebnisse bestätigten unsere Hypothese und erlaubten uns, die Referenzwerte für die KZV240 zu berechnen. Es ist extrem wichtig für jeden Arzt, der den neuen Algorithmus in seiner alltäglichen Praxis verwendet, zu wissen, dass die Normalwerte für die KZV240 (nicht nur der Mittelwert, sondern auch die 95. Perzentile) unterhalb der bislang etablierten cut-off-Werte für die KZV16 liegen. Dies unterstreicht die Tatsache, dass bei der Interpretation der KZV des cCTGs der verwendete Algorithmus berücksichtigt werden sollte. Ansonsten besteht die Gefahr von unnötigen, iatrogenen, vorzeitigen Entbindungen mit allen damit verbundenen Risiken

Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects

Non Invasive Foetal Monitoring with a Combined ECG - PCG System

Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative

Intrapartum cardiotocography patterns observed in suspected clinical and subclinical chorioamnionitis in term fetuses.

AIM: To evaluate the cardiotocography (CTG) features observed in suspected intrapartum chorioamnionitis in term fetuses according to the recently suggested criteria for the pathophysiological interpretation of the fetal heart rate and their correlation with perinatal outcomes. METHODS: Retrospective analysis of nonconsecutive CTG traces. 'CTG chorioamnionitis' was diagnosed either based on a persistent rise in the baseline for the given gestation or on a persistent increase in the baseline fetal heart rate during labor >10% without preceding CTG signs of hypoxia and in the absence of maternal pyrexia. Perinatal outcomes were compared among cases with no sign of chorioamnionitis, in those with only CTG features suspicious for chorioamnionitis and in those who developed clinical chorioamnionitis. RESULTS: Two thousand one hundred and five CTG traces were analyzed. Of these, 356 fulfilled the criteria for "CTG chorioamnionitis". Higher rates of Apgar <7 at 1 and 5 min (21.6% vs 9.0% and 9.8% vs 2.0%, respectively, P < 0.01 for both) and lower umbilical artery pH (7.14 ± 0.11 vs 7.19 ± 0.11, P < 0.01) and an over fivefold higher rate of neonatal intensive care unit admission (16.6% vs 2.9%, P < 0.01) were noted in the 'CTG chorioamnionitis' group. Differences in the incidence of abnormal CTG patterns were noted between cases who eventually had clinical evidence of chorioamnionitis (89/356) and those showing CTG features suspicious for chorioamnionitis in the absence of clinical evidence of chorioamnionitis (267/356). CONCLUSION: Intrapartum CTG features of suspected chorioamnionitis are associated with adverse perinatal outcomes

Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models

Cardiotocography (CTG) is a key element when it comes to monitoring fetal well-being. Obstetricians use it to observe the fetal heart rate (FHR) and the uterine contraction (UC). The goal is to determine how the fetus reacts to the contraction and whether it is receiving adequate oxygen. If a problem occurs, the physician can then respond with an intervention. Unfortunately, the interpretation of CTGs is highly subjective and there is a low inter- and intra-observer agreement rate among practitioners. This can lead to unnecessary medical intervention that represents a risk for both the mother and the fetus. Recently, computer-assisted diagnosis techniques, especially based on artificial intelligence models (mostly supervised), have been proposed in the literature. But, many of these models lack generalization to unseen/test data samples due to overfitting. Moreover, the unsupervised models were applied to a very small portion of the CTG samples where the normal and abnormal classes are highly separable. In this work, deep unsupervised learning approaches, trained in a semi-supervised manner, are proposed for anomaly detection in CTG signals. The GANomaly framework, modified to capture the underlying distribution of data samples, is used as our main model and is applied to the CTU-UHB dataset. Unlike the recent studies, all the CTG data samples, without any specific preferences, are used in our work. The experimental results show that our modified GANomaly model outperforms state-of-the-arts. This study admit the superiority of the deep unsupervised models over the supervised ones in CTG abnormality detection

Different aspects of electronic fetal monitoring during labor

Background: Cardiotocography (CTG) is a tool to assess fetal well-being during labor and to detect early signs of fetal distress and thereby enable timely interventions to reduce neonatal morbidity and mortality. CTG is associated with shortcomings; poor reliability in interpretation, low specificity with a high proportion of false positive tracings indicating fetal distress when not accurate, no proven effect on rare severe outcomes such as mortality and cerebral palsy, but rather contributing to an increased risk of operative delivery. The aims of this thesis was to determine I) if an extended CTG education could lead to better reliability in interpretation compared to a national standard education, II) if a computerized algorithm could be developed with precision in detecting and quantitating decelerations on CTG, III) if deceleration area was a better predictor of fetal acidemia during labor than deceleration depth and duration, IV) the proportion of fetuses with undetected small for gestational age (SGA) in a low-risk population, comparing women that present with normal CTG at admission to labor (admCTG) to those with abnormal admCTG and to compare neonatal outcomes in the two groups stratified on SGA or non-SGA. Material and methods: The CTG tracings used in paper I-III were extracted from a previous cohort of women in labor, from Karolinska University Hospital, Sweden. All women had undergone fetal blood sampling (FBS) during labor due to suspicious CTG patterns. Six obstetricians from two different hospitals were used as observers in paper I. Inter- and intra-observer reliability using Cohen’s and Fleiss kappa was determined for different parameters assessed on CTG. In paper II two obstetricians visually analyzed CTG tracings with variable decelerations and specified duration, depth and area for each deceleration. The computerized algorithm analyzed and quantified the same CTG traces and was compared to the observers using intra-class correlation and Bland-Altman analysis. In paper III the predictive value of deceleration area, duration, and depth for fetal acidemia, measured as lactate concentration at FBS, was explored using receiver operating characteristics, area under curve (ROC AUC). In paper IV, a register-based study, the risk of SGA in relation to the result of admCTG, normal vs abnormal was assessed in low-risk pregnancies. Neonatal outcomes were also determined by multiple logistic regression analysis. Results: I) The inter- and intra-observer reliability was moderate to excellent at both departments, kappa 0.41-0.93. The department with extended education reached significantly higher interobserver agreement for two of six CTG parameters assessed. II) Computerized assessment of decelerations on CTG compared to visual observers reached excellent intraclass correlation (0.89-0.95) and low bias in Bland-Altman analysis, comparable to that between the two observers. III) The deceleration measures with the best prediction of fetal acidemia was cumulative deceleration area and duration, ROC AUC 0.682 and 0.683 respectively compared to deceleration depth 0.631. IV) The proportion of SGA was two-fold higher among neonates presenting with abnormal admCTG (18.6%) compared to normal admCTG (9.7%). The risk of composite severe adverse neonatal complications was substantially higher in the group with abnormal admCTG/SGA compared to normal admCTG/non-SGA, adjusted odds ratio 23.7 (95% confidence interval 9.8-57.3) Conclusion: Inter- and intra-observer agreement was better than expected at both departments studied and extended education might have an impact on interpretation reliability. A novel computerized algorithm for CTG assessment has high precision in detecting and quantifying decelerations. Cumulative deceleration area and duration are better predictors of fetal acidemia than deceleration depth. In presumed low-risk pregnancies there is a group of undetected SGA fetuses that more often present with abnormal admCTG and are at higher risks of neonatal complications