论文标题
基于深度学习的无分段性声明图谱图的分类，利用转移学习
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning
论文作者
论文摘要
心血管疾病（CVD）是世界各地死亡的主要原因。心脏杂音是在听诊过程中检测到的最常见的异常。两个广泛使用的公开使用的Phonocartiogram（PCG）数据集来自Physionet/CINC（2016）和Pascal（2011）挑战。在数据获取，临床协议，数字存储和信号质量的工具方面，数据集有显着差异，使得处理和分析变得具有挑战性。在这项工作中，我们使用了基于短时的傅立叶变换（STFT）频谱图来学习正常和异常PCG信号的代表性模式。 Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL数据集。我们提出了一种新颖，较不复杂且相对较轻的自定义CNN模型，用于对Physionet，组合和Pascal数据集进行分类。第一项研究的准确性，敏感性，特异性，精度和F1得分分别为95.4％，96.3％，92.4％，97.6％和96.98％，而第二项研究表明准确性，敏感性，特异性，精度和F1分数和94.2％，95.5％，90.3％，90.3％，96.8％和96.8％和96.8％和96.8％和96.8％和96.8％的分数。最后，第三项研究显示，使用转移学习方法的嘈杂的帕斯卡数据集的精度为98.29％。这三种提出的方法通过实现相对较高的分类精度和精确度来优于最近的大多数竞争研究，这使得它们适合使用PCG信号筛选CVD。
Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. In this work, we have used short-time Fourier transform (STFT) based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets. The first study achieves an accuracy, sensitivity, specificity, precision and F1 score of 95.4%, 96.3%, 92.4%, 97.6% and 96.98% respectively while the second study shows accuracy, sensitivity, specificity, precision and F1 score of 94.2%, 95.5%, 90.3%, 96.8% and 96.1% respectively. Finally, the third study shows a precision of 98.29% on the noisy PASCAL dataset with transfer learning approach. All the three proposed approaches outperform most of the recent competing studies by achieving comparatively high classification accuracy and precision, which make them suitable for screening CVDs using PCG signals.
