Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels from different label groups, including murmur's timing, pitch, grading, quality, and shape. Our experiment results show that the proposed method has achieved outstanding performance on the holdout data for the multi-labelling task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level, and an overall accuracy=0.969 at the patient's recording level.
翻译:心声诊断和分类在发现心血管紊乱方面起着关键作用,特别是当远程诊断成为标准的临床实践时。目前的工作大部分是为单一类别设计的,以听得见的健全分类任务。为了进一步扩大自动心脏声学诊断景观的景观,这项工作提议了一个深厚的多标签学习模式,可以自动用不同标签组的标签对心脏声录音进行注解,包括杂音的时机、声调、分级、质量和形状。我们的实验结果表明,拟议的方法在多标签任务(敏感度=0.990、特殊性=0.999、F1=0.990)的延迟数据上取得了杰出的性能,在分层一级,总体精度=0.969的病人记录水平。