Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo

Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red longshort- term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM.Este trabajo ha sido financiado por el Ministerio de Economía y Competitividad mediante el proyecto TEC2015-64678R junto con el Fondo Europeo de Desarrollo Regional (FEDER), así como por la UPVEHU mediante el proyecto EHU16/18

Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo

Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red longshort- term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM.Este trabajo ha sido financiado por el Ministerio de Economía y Competitividad mediante el proyecto TEC2015-64678R junto con el Fondo Europeo de Desarrollo Regional (FEDER), así como por la UPVEHU mediante el proyecto EHU16/18

Endothelial Progenitor Cells as a Potential Biomarker in Interstitial Lung Disease Associated with Rheumatoid Arthritis

Interstitial lung disease (ILD) increases morbidity and mortality in patients with rheumatoid arthritis (RA). Although the pathogenesis of ILD associated with RA (RA-ILD(+)) remains poorly defined, vascular tissue is crucial in lung physiology. In this context, endothelial progenitor cells (EPC) are involved in endothelial tissue repair. However, little is known about their implication in RA-ILD(+). Accordingly, we aimed to investigate the potential role of EPC related to endothelial damage in RA-ILD(+). EPC quantification in peripheral blood from 80 individuals (20 RA-ILD(+) patients, 25 RA-ILD(-) patients, 21 idiopathic pulmonary fibrosis (IPF) patients, and 14 healthy controls) was performed by flow cytometry. EPC were considered as CD34(+), CD45(low), CD309(+) and CD133(+). A significant increase in EPC frequency in RA-ILD(+) patients, as well as in RA-ILD(-) and IPF patients, was found when compared with controls (p < 0.001, p = 0.02 and p < 0.001, respectively). RA-ILD(+) patients exhibited a higher EPC frequency than the RA-ILD(-) ones (p = 0.003), but lower than IPF patients (p < 0.001). Our results suggest that EPC increase may represent a reparative compensatory mechanism in patients with RA-ILD(+). The degree of EPC frequency may help to identify the presence of ILD in RA patients and to discriminate RA-ILD(+) from IPF

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

Role of MUC1 rs4072037 polymorphism and serum KL-6 levels in patients with antisynthetase syndrome

Mucin 1/Krebs von den Lungen-6 (KL-6) is proposed as a serum biomarker of several interstitial lung diseases (ILDs), including connective tissue disorders associated with ILD. However, it has not been studied in a large cohort of Caucasian antisynthetase syndrome (ASSD) patients. Consequently, we assessed the role of MUC1 rs4072037 and serum KL-6 levels as a potential biomarker of ASSD susceptibility and for the differential diagnosis between patients with ILD associated with ASSD (ASSD-ILD?+) and idiopathic pulmonary fibrosis (IPF). 168 ASSD patients (149 ASSD-ILD?+), 174 IPF patients and 523 healthy controls were genotyped for MUC1 rs4072037 T?>?C. Serum KL-6 levels were determined in a subgroup of individuals. A significant increase of MUC1 rs4072037 CC genotype and C allele frequencies was observed in ASSD patients compared to healthy controls. Likewise, MUC1 rs4072037 TC and CC genotypes and C allele frequencies were significantly different between ASSD-ILD+ and IPF patients. Additionally, serum KL-6 levels were significantly higher in ASSD patients compared to healthy controls. Nevertheless, no differences in serum KL-6 levels were found between ASSD-ILD+ and IPF patients. Our results suggest that the presence of MUC1 rs4072037 C allele increases the risk of ASSD and it could be a useful genetic biomarker for the differential diagnosis between ASSD-ILD+ and IPF patients

Role of MUC1 rs4072037 polymorphism and serum KL-6 levels in patients with antisynthetase syndrome

Mucin 1/Krebs von den Lungen-6 (KL-6) is proposed as a serum biomarker of several interstitial lung diseases (ILDs), including connective tissue disorders associated with ILD. However, it has not been studied in a large cohort of Caucasian antisynthetase syndrome (ASSD) patients. Consequently, we assessed the role of MUC1 rs4072037 and serum KL-6 levels as a potential biomarker of ASSD susceptibility and for the differential diagnosis between patients with ILD associated with ASSD (ASSD-ILD +) and idiopathic pulmonary fibrosis (IPF). 168 ASSD patients (149 ASSD-ILD +), 174 IPF patients and 523 healthy controls were genotyped for MUC1 rs4072037 T > C. Serum KL-6 levels were determined in a subgroup of individuals. A significant increase of MUC1 rs4072037 CC genotype and C allele frequencies was observed in ASSD patients compared to healthy controls. Likewise, MUC1 rs4072037 TC and CC genotypes and C allele frequencies were significantly different between ASSD-ILD+ and IPF patients. Additionally, serum KL-6 levels were significantly higher in ASSD patients compared to healthy controls. Nevertheless, no differences in serum KL-6 levels were found between ASSD-ILD+ and IPF patients. Our results suggest that the presence of MUC1 rs4072037 C allele increases the risk of ASSD and it could be a useful genetic biomarker for the differential diagnosis between ASSD-ILD+ and IPF patients

The Mars Environmental Dynamics Analyzer, MEDA. A Suite of Environmental Sensors for the Mars 2020 Mission

86 pags., 49 figs., 24 tabs.NASA’s Mars 2020 (M2020) rover mission includes a suite of sensors to monitor current environmental conditions near the surface of Mars and to constrain bulk aerosol properties from changes in atmospheric radiation at the surface. The Mars Environmental Dynamics Analyzer (MEDA) consists of a set of meteorological sensors including wind sensor, a barometer, a relative humidity sensor, a set of 5 thermocouples to measure atmospheric temperature at ∼1.5 m and ∼0.5 m above the surface, a set of thermopiles to characterize the thermal IR brightness temperatures of the surface and the lower atmosphere. MEDA adds a radiation and dust sensor to monitor the optical atmospheric properties that can be used to infer bulk aerosol physical properties such as particle size distribution, non-sphericity, and concentration. The MEDA package and its scientific purpose are described in this document as well as how it responded to the calibration tests and how it helps prepare for the human exploration of Mars. A comparison is also presented to previous environmental monitoring payloads landed on Mars on the Viking, Pathfinder, Phoenix, MSL, and InSight spacecraft.This work has been funded by the Spanish Ministry of Economy and Competitiveness, through the projects No. ESP2014-54256-C4-1-R (also -2-R, -3-R and -4-R) and AYA2015-65041-P; Ministry of Science, Innovation and Universities, projects No. ESP2016-79612-C3-1-R (also -2-R and -3-R), ESP2016-80320-C2-1-R, RTI2018-098728-B-C31 (also -C32 and -C33) and RTI2018-099825-B-C31; Instituto Nacional de Técnica Aeroespacial; Ministry of Science and Innovation’s Centre for the Development of Industrial Technology; Grupos Gobierno Vasco IT1366-19; and European Research Council Consolidator Grant no 818602. The US co-authors performed their work under sponsorship from NASA’s Mars 2020 project, from the Game Changing Development program within the Space Technology Mission Directorate and from the Human Exploration and Operations Directorate

The Rise and Fall of "Respectable" Spanish Liberalism, 1808-1923: An Explanatory Framework

The article focuses on the reasons behind both the consolidation of what I have termed “respectable” liberalism between the 1830s and the 1840s and its subsequent decline and fall between 1900 and 1923. In understanding both processes I study the links established between “respectable” liberals and propertied elites, the monarchy, and the Church. In the first phase these links served to consolidate the liberal polity. However, they also meant that many tenets of liberal ideology were compromised. Free elections were undermined by the operation of caciquismo, monarchs established a powerful position, and despite the Church hierarchy working with liberalism, the doctrine espoused by much of the Church was still shaped by the Counter-Reformation. Hence, “respectable” liberalism failed to achieve a popular social base. And the liberal order was increasingly denigrated as part of the corrupt “oligarchy” that ruled Spain. Worse still, between 1916 and 1923 the Church, monarch, and the propertied elite increasingly abandoned the liberal Monarchist Restoration. Hence when General Primo de Rivera launched his coup the rug was pulled from under the liberals’ feet and there was no one to cushion the fall

Land- and water-based exercise intervention in women with fibromyalgia: the al-andalus physical activity randomised controlled trial

Background The al-Andalus physical activity intervention study is a randomised control trial to investigate the effectiveness of a land- and water-based exercise intervention for reducing the overall impact of fibromyalgia (primary outcome), and for improving tenderness and pain-related measures, body composition, functional capacity, physical activity and sedentary behaviour, fatigue, sleep quality, health-related quality of life, and cognitive function (secondary outcomes) in women with fibromyalgia. Methods/Design One hundred eighty women with fibromyalgia (age range: 35-65 years) will be recruited from local associations of fibromyalgia patients in Andalucía (Southern Spain). Patients will be randomly assigned to a usual care (control) group (n = 60), a water-based exercise intervention group (n = 60) or a land-based exercise intervention group (n = 60). Participants in the usual care group will receive general physical activity guidelines and participants allocated in the intervention groups will attend three non-consecutive training sessions (60 min each) per week during 24 weeks. Both exercise interventions will consist of aerobic, muscular strength and flexibility exercises. We will also study the effect of a detraining period (i.e., 12 weeks with no exercise intervention) on the studied variables. Discussion Our study attempts to reduce the impact of fibromyalgia and improve patients' health status by implementing two types of exercise interventions. Results from this study will help to assess the efficacy of exercise interventions for the treatment of fibromyalgia. If the interventions would be effective, this study will provide low-cost and feasible alternatives for health professionals in the management of fibromyalgia. Results from the al-Andalus physical activity intervention will help to better understand the potential of regular physical activity for improving the well-being of women with fibromyalgia.This study was supported by the Consejeria de Turismo, Comercio y Deporte (CTCD-201000019242-TRA), the Spanish Ministry of Science and Innovation (I + D + I DEP2010-15639, grants: BES-2009-013442, BES-2011-047133, RYC-2010-05957, RYC-2011-09011), the Swedish Heart-Lung Foundation (20090635), the Spanish Ministry of Education (AP-2009-3173), Granada Research of Excelence Initiative on Biohealth (GREIB), Campus BioTic, University of Granada, Spain and European University of Madrid. Escuela de Estudios Universitarios Real Madrid. 2010/04RM

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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