An Accurate Shock Advise Algorithm for Use During Piston-Driven Chest Compressions

Mechanically delivered chest compressions induce artifacts in the ECG that can lead to an incorrect diagnosis of the shock advice algorithms implemented in the defibrillators. This forces the rescuer to stop cardiopulmonary resuscitation (CPR) compromising circulation and thus reducing the probability of survival. This paper introduces a new approach for a reliable rhythm analysis during mechanical compressions which consists of an artifact supression filter based on the recursive least squares algorithm, and a shock/no-shock decision algorithm based on machine learning techniques that uses features obtained from the filtered ECG. Data were collected from 230 out-of-hospital cardiac arrest patients treated with the LUCAS CPR device. The underlying rhythms were annotated in artifact-free intervals by consesus of expert resuscitation rhythm reviewers. Shock/no-shock diagnoses obtained through the decision algorithm were compared with the rhythm annotations to obtain the sensitivity (Se), specificity (Sp) and balanced accuracy (BAC) of the method. The results obtained were: 94.7% (Se), 97.1% (Sp) and 95.9% (BAC)

Removing piston-driven mechanical chest compression artefacts from the ECG

Piston-driven mechanical chest compression (CC) devices induce a quasi-periodic artefact in the ECG, making rhythm diagnosis unreliable. Data from 230 out-of-hospital cardiac arrest (OHCA) patients were collected in which CCs were delivered using the piston driven LUCAS-2 device. Underlying rhythms were annotated by expert reviewers in artefact-free intervals. Two artefact removal methods (filters) were introduced: a static solution based on Goertzel’s algorithm, and an adaptive solution based on a Recursive Least Squares (RLS) filter. The filtered ECG was diagnosed by a shock/no-shock decision algorithm used in a commercial defibrillator and compared with the rhythm annotations. Filter performance was evaluated in terms of balanced accuracy (BAC), the mean of sensitivity (shockable) and specificity (nonshockable). Compared to the unfiltered signal, the static filter increased BAC by 20 points, and the RLS filter by 25 points. Adaptive filtering results in 99.0% sensitivity and 87.3% specificity

Foveal Pit Morphology Characterization: A Quantitative Analysis of the Key Methodological Steps

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered

Influencia de la frecuencia respiratoria inducida en los valores HRV

The study of Place Cells, hippocampal neurons tuned to spatial locations in the environment, is central to elucidate how the brain encodes and retrieves spatial information. Advances in genetic and imaging technologies have allowed keeping track of the dynamics of large ensembles of Place Cells across multiple days in mice. As the brain processes information at the neuronal population level, novel recording techniques such as in-vivo calcium imaging have the potential to unveil the mechanisms underlying the dynamics of place coding. However, with new recording paradigms comes the need to standardize and optimize the processing and first analysis stages of the data. In this work, we present our efforts in building a pipeline to process, extract, filter, track and analyze Place Cells from mice calcium imaging recordings in a linear-track experiment. To validate the pipeline, we show accurate prediction of the animal actions from the processed neural recordings. Finally, building on the previous steps, we present some tentative results on Place Cell turnover and the relation between predictive-accuracy and noise correlations

Solución Multietapa para Diagnóstico del Ritmo Cardíaco durante la Resucitación Cardiopulmonar

Las compresiones torácicas durante la terapia de resucitación cardiopulmonar (RCP) inducen artefactos en el ECG comprometiendo el diagnóstico de los algoritmos de análisis de ritmo. El objetivo de este trabajo es diseñar un método que diagnostique con precisión el ritmo durante la RCP evitando así tener que interrumpir la terapia. Para ello se diseñó un algoritmo multi-etapa (AME) que incluye dos filtros para la supresión del artefacto basados en un algoritmo recursivo de mínimos cuadrados (RLS), el algoritmo de análisis de ritmo de un desfibrilador comercial y un clasificador de ritmos basado en la pendiente del ECG. Se usó una base de datos compuesta por 87 ritmos desfibrilables y 285 no-desfibrilables adquiridos de pacientes en parada cardiorrespitatoria extra-hospitalaria. Para la optimización y validación de la solución AME los datos se dividieron aleatoriamente por pacientes en un conjunto de entrenamiento (70%) y otro de prueba (30%). Este proceso se repitió 500 veces para estimar la distribución estadística de la sensibilidad (Se), especificidad (Sp) y precisión (Acc) de la solución AME. Los valores medios (desviación estándar) de Se, Sp y Acc fueron 92.1% (6.0), 92.4% (2.9) y 92.2% (3.0), respectivamente. La solución mejora resultados anteriores por hasta 5 puntos de precisión

Seinalearen prozesatze digitalaren erronkak kanpoko desfibrilagailu automatikoaren inguruan

Bihotz-biriketako gelditzearen aurrean berpiztea honako bi ekintza nagusiren mendekoa da: bihotz-biriketako berpizte-masajea eta bentrikulu-erritmoaren desfibrilazioa, kanpoko desfibrilagailu automatiko baten bidez. Tresna horiek bihotzaren erritmo hilgarriak detektatzen dituzte pazientearen elektrokardiograma digitalki prozesatuz; ildo horretatik, azkeneko urteetan hiru erronka nabarmendu daitezke. Umeen (1 eta 8 urte bitartekoen) erritmoen sailkatze egokia lortzea da lehengoa. Bi sailkatze-parametro aurkezten eta ebaluatzen dira lan honetan. Horretarako, helduen eta umeen bihotz-erritmo desberdinez osatutako datu-basea aztertzen da, sentsibilitatea eta espezifikotasuna adinaren arabera neurtuz. Bigarrenak berpizte-masajea ematearekin batera analisi fidagarria egitea du helburu. Erreferentzia-seinaleak erabilita eta soilik elektrokardiograma erabilita, iragazketa moldakorrekin lortutako emaitzak konparatzen dira. Azkenik, hirugarren erronkaren inguruan, desfibrilazioaren arrakasta aurresateko metodoetara hurbilketa egiten da. Erronka horien guztien helburua bihotz-biriketako geldiunean dagoen gizakiaren bizi-aukera handitzea da

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

Seinalearen prozesatze digitalaren erronkak kanpoko desfibrilagailu automatikoaren inguruan

Bihotz-biriketako gelditzearen aurrean berpiztea honako bi ekintza nagusiren mendekoa da: bihotz-biriketako berpizte-masajea eta bentrikulu-erritmoaren desfibrilazioa, kanpoko desfibrilagailu automatiko baten bidez. Tresna horiek bihotzaren erritmo hilgarriak detektatzen dituzte pazientearen elektrokardiograma digitalki prozesatuz; ildo horretatik, azkeneko urteetan hiru erronka nabarmendu daitezke. Umeen (1 eta 8 urte bitartekoen) erritmoen sailkatze egokia lortzea da lehengoa. Bi sailkatze-parametro aurkezten eta ebaluatzen dira lan honetan. Horretarako, helduen eta umeen bihotz-erritmo desberdinez osatutako datu-basea aztertzen da, sentsibilitatea eta espezifikotasuna adinaren arabera neurtuz. Bigarrenak berpizte-masajea ematearekin batera analisi fidagarria egitea du helburu. Erreferentzia-seinaleak erabilita eta soilik elektrokardiograma erabilita, iragazketa moldakorrekin lortutako emaitzak konparatzen dira. Azkenik, hirugarren erronkaren inguruan, desfibrilazioaren arrakasta aurresateko metodoetara hurbilketa egiten da. Erronka horien guztien helburua bihotz-biriketako geldiunean dagoen gizakiaren bizi-aukera handitzea da

Spatial characterization of the effect of age and sex on macular layer thicknesses and foveal pit morphology

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section