论文标题
深色的心脏选择 - 一个结合的深度学习模型多列表
Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling
论文作者
论文摘要
心脏声音诊断和分类在检测心血管疾病中起着至关重要的作用，尤其是当远程诊断成为标准临床实践时。当前的大多数工作都是为基于单个类别的听觉声音分类任务而设计的。为了进一步扩展自动心脏声音诊断景观的景观，这项工作提出了一个深层的多标签学习模型，可以自动用不同标签组的标签来自动注释心脏声音录音，包括Murmur的时间安排，音高，评分，质量，质量和形状。我们的实验结果表明，该提出的方法在敏感性= 0.990，特异性= 0.999，f1 = 0.990的多标签任务上取得了出色的性能，在分段水平上进行了f1 = 0.990，总体准确性= 0.969在患者的记录水平上。
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.
