Castells Ramón, Francisco Sales

Organizational Units

Organizational Unit

Departamento de Ingeniería Electrónica

Organizational Unit

Instituto Universitario de Tecnologías de la Información y Comunicaciones

Organizational Unit

Escuela Politécnica Superior de Gandia

Search Results

Now showing 1 - 10 of 37

Rectificador de media onda con filtro C y estabilizador Zener
(Universitat Politècnica de València, 2020-06-09T05:39:21Z) Castells Ramón, Francisco Sales; Vives Gilabert, Yolanda; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia
En este laboratorio virtual se muestra la forma de onda en cada etapa de un rectificador de media onda con filtro C y estabilizador Zener: - Señal de entrada - Señal rectificada - Señal en el condensador - Señal estabilizada a la salida
Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach
(2021-09-15) Jiménez Serrano, Santiago; RODRIGO BORT, MIGUEL; Calvo Sáiz, Conrado Javier; Castells Ramón, Francisco Sales; Millet Roig, José; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería de Telecomunicación; Departamento de Sistemas Informáticos y Computación; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Técnica Superior de Ingeniería Industrial; Escuela Politécnica Superior de Gandia; Escuela Técnica Superior de Ingeniería Informática; Instituto de Instrumentación para Imagen Molecular
[EN] Although standard 12-lead ECG is the primary technique in cardiac diagnostic, detecting different cardiac diseases using single or reduced number of leads is still challenging. The purpose of our team, itaca-UPV, is to provide a method able to classify ECG records using minimal lead information in the context of the 2021 PhysioNet/Computing in Cardiology Challenge, also using only a single-lead. We resampled and filtered the ECG signals, and extracted 109 features mostly based on Hearth Rhythm Variability (HRV). Then, we used selected features to train one feed-forward neural network (FFNN) with one hidden layer for each class using a One-vs-Rest approach, thus allowing each ECG to be classified as belonging to none or more than one class. Finally, we performed a 3-fold cross validation to assess the model performance. Our classifiers received scores of 0.34, 0.34, 0.27, 0.30, and 0.34 (ranked 26th, 21th, 29th, 25th, and 22th out of 39 teams) for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions. Accuracy in detection can be improved adding more disease-specific features.
Curva de Lissajous
(Universitat Politècnica de València, 2020-06-09T05:39:36Z) Castells Ramón, Francisco Sales; Vives Gilabert, Yolanda; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia
Este laboratorio virtual representa la curva de Lissajous correspondiente a dos señales senoidales con un desfase entre ellas.
An open access database for the evaluation of heart sound algorithms
(IOP Publishing, 2016-11-21) Liu, Chengyu; Springer, David Castells; Moody, Benjamin; Abad Juan, Ricardo Carlos; Li, Qiao; Moody, Benjamin ; Chorro, Francisco J.; Castells Ramón, Francisco Sales; Millet Roig, José; Silva, Ikaro; Johnson, Alistair E. W.; Syed, Zeeshan; Schmidt, Samuel E.; Papadaniil, Chrysa D.; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería de Telecomunicación; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia; National Institutes of Health, EEUU
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.
Design of loudspeaker enclosures: bass-reflex
(Universitat Politècnica de València, 2020-01-07T10:42:55Z) Castells Ramón, Francisco Sales; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia
This virtual lab represents the curves of a Bass-Reflex design from the electromechanical parameters of a driver, maximal ratings (including maximum linear displacement and maximum electrical power) and tuning parameters of the vented enclosure (fb, Vb and QL).
Sound radiation in loudspeakers: frequency response
(Universitat Politècnica de València, 2019-12-27T10:02:05Z) Castells Ramón, Francisco Sales; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia
Given the electromechanical parameters of a speaker, this virtual lab represents the sound pressure level (at 1m) as a function of frequency. Four curves are represented, according to different angles: on-axis, 30º, 60º an 90º.
Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review
(Begell House Inc., 2021) Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Kotas, Marian; Castells Ramón, Francisco Sales; Moron, Tomasz; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia; Universidad Tecnológica de Bolívar
[EN] Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performance
Quantification of anaesthetic effects on atrial fibrillation rate by partial least-squares
(IOP Publishing: Hybrid Open Access, 2012-10) Cervigón Abad, Raquel; Moreno, J.; Reilly, Richard; Perez-Villacastin, J.; Castells Ramón, Francisco Sales; Departamento de Ingeniería Electrónica; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia; Ministerio de Ciencia e Innovación
The mechanism underlying atrial fibrillation (AF) remains poorly understood. Multiple wandering propagation wavelets drifting through both atria under hierarchical models are not understood. Some pharmacological drugs, known as antiarrhythmics, modify the cardiac ionic currents supporting the fibrillation process within the atria and may modify the AF propagation dynamics terminating the fibrillation process. Other medications, theoretically non-antiarrhythmic, may slightly affect the fibrillation process in non-defined mechanisms. We evaluated whether the most commonly used anaesthetic agent, propofol, affects AF patterns. Partial least-squares (PLS) analysis was performed to reduce significant noise into the main latent variables to find the differences between groups. The final results showed an excellent discrimination between groups with slow atrial activity during the propofol infusion. © 2012 Institute of Physics and Engineering in Medicine. © 2012 Institute of Physics and Engineering in Medicine.
Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement
(Elsevier, 2020-05) Vives-Gilabert, Yolanda; Zorio, Esther; Sanz-Sánchez, Jorge; Calvillo-Batllés, Pilar; Millet Roig, José; Castells Ramón, Francisco Sales; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería de Telecomunicación; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia; Instituto de Salud Carlos III; Ministerio de Economía y Empresa; European Regional Development Fund
[EN] Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naive Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.
Pulmonary Vein Activity Organization to Determine Atrial Fibrillation Recurrence: Preliminary Data from a Pilot Study
(MDPI AG, 2020-10) Cervigón, Raquel; Moreno, Javier; Millet Roig, José; Pérez-Villacastín, Julián; Castells Ramón, Francisco Sales; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería de Telecomunicación; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Politécnica Superior de Gandia; Generalitat Valenciana; AGENCIA ESTATAL DE INVESTIGACION; Ministerio de Economía y Competitividad
[EN] Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.