Cardiovascular diseases include a very long series of diseases that afflict many people in the world. Many of them can be diagnosed by listening to the heartbeat, however in the face of the large number of patients performing checks, great delays can occur given the few doctors available. In this paper we propose a convolutional neural network aimed to discriminate regular heartbeats from abnormal ones, making a first screening of patients. Moreover we provide classification explainability through activation maps. The experimental analysis consists of 3240 (regular and abnormal) heartbeat phonocardiogram signals, showing the effectiveness of the proposed method.
Deep Learning for Heartbeat Phonocardiogram Signals Explainable Classification
Cesarelli M.;Mercaldo F.;Santone A.
2022-01-01
Abstract
Cardiovascular diseases include a very long series of diseases that afflict many people in the world. Many of them can be diagnosed by listening to the heartbeat, however in the face of the large number of patients performing checks, great delays can occur given the few doctors available. In this paper we propose a convolutional neural network aimed to discriminate regular heartbeats from abnormal ones, making a first screening of patients. Moreover we provide classification explainability through activation maps. The experimental analysis consists of 3240 (regular and abnormal) heartbeat phonocardiogram signals, showing the effectiveness of the proposed method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
