Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.Web of Science71art. no. 20

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values mu < 0.1 and values of +/- 1.96 sigma < 0.1).Web of Science168art. no. e025615

Non-invasive fetal electrocardiogram extraction based on novel hybrid method for intrapartum ST segment analysis

This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditional - gold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method.Web of Science9286312860

Crime and the Economy in Mexican States : Heterogeneous Panel Estimates (1993-2012)

This paper studies the transmission of crime shocks to the economy in a sample of 32 Mexican states over the period from 1993 to 2012. The paper uses a panel structural VAR approach which accounts for the heterogeneity of the dynamic state level responses in GDP, FDI and international migration flows, and measures the transmission via the impulse response of homicide rates. The approach also allows the study of the pattern of economic responses among states. In particular, the percentage of GDP devoted to new construction and the perception of public security are characteristics that are shown to be associated with the sign and magnitude of the responses of economic variables to crime shocks

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

Adolescent attitudes about altruistic suicide

A survey of 1,019 Polish high school students found that some forms of altruistic suicide were viewed favorably, whereas other forms were viewed very negatively. A relatively more negative view in general toward altruistic suicide was associated with being more religious, whereas a relatively more positive view was associated with personal involvement in suicidal behavior and having friends who had committed suicide

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