Detection of abnormal cardiac response patterns in cardiac tissue using deep learning
Fecha de publicación
2022ISSN
2227-7390
Resumen
This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
Inglés
Materias (CDU)
61 - Medicina
Palabras clave
Aprenentatge profund
Codificador automàtic
Teixit cardíac
Electrofisiologia
Electroestimulació
Detecció d'anomalies
Xarxa neuronal recurrent
Memòria a curt termini
Model de ratolí CD-1
Aprendizaje profundo
Codificador automático
Tejido cardiaco
Electrofisiología
Electroestimulación
Detección de anomalías
Red neuronal recurrente
Memoria larga a corto plazo
Modelo de ratón CD-1
Deep learning
Autoencoder
Cardiac tissue
Electrophysiology
Electrostimulation
Anomaly detection
Recurrent neural network
Long short-term memory
CD-1 mouse model
Páginas
21
Publicado por
MDPI
Colección
10;15
Publicado en
Mathematics
Citación
Marimon Serra, Xavier; Traserra, Sara; Jimenez, Marcel [et al.]. Detection of abnormal cardiac response patterns in cardiac tissue using deep learning. Mathematics, 2022, 10(15), 2786. Disponible en: <https://www.mdpi.com/2227-7390/10/15/2786>. Fecha de acceso: 14 oct. 2022. DOI: 10.3390/math10152786.
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