The CirCor DigiScope dataset: from murmur detection to murmur classification

• Autor(a) principal: Oliveira, Jorge
• Data de Publicação: 2021
• Outros Autores: Renna, Francesco, Costa, Paulo Dias, Nogueira, Marcelo, Oliveira, Cristina, Ferreira, Carlos, Jorge, Alípio, Mattos, Sandra, Hatem, Thamine, Tavares, Thiago, Elola, Andoni, Rad, Ali Bahrami, Sameni, Reza, Clifford, Gari D, Coimbra, Miguel T.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/11328/4087
• topic: Cardiac auscultation

Cardiac auscultation is one of the most costeffective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.