Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, Republic of Korea.
Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
PLoS One. 2024 Jul 15;19(7):e0305404. doi: 10.1371/journal.pone.0305404. eCollection 2024.
This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in signal characteristics. We used the Continuous Wavelet Transform to analyze changing frequency characteristics over time and to allocate frequencies appropriately between the low-frequency and audible frequency bands. We used a Convolutional Neural Network (CNN) and deep-learning (DL) for image classification, and a CNN equipped with long short-term memory to enable sequential feature extraction. The accuracy of the learning model was validated using the PhysioNet 2016 CinC dataset, then we used our collected dataset to show that incorporating low-frequency components in the dataset increased the DL model's accuracy by 2% and sensitivity by 4%. Furthermore, the LSTM layer was 0.8% more accurate than the dense layer.
本研究旨在探讨心音低频成分的纳入是否能提高心血管疾病诊断的准确性、灵敏度和特异性。我们采用标准化的测量方法，尽量减少信号特征的变化。我们使用连续小波变换（CWT）来分析随时间变化的频率特征，并在低频和可听频带之间适当地分配频率。我们使用卷积神经网络（CNN）和深度学习（DL）进行图像分类，并使用带有长短期记忆（LSTM）的 CNN 进行顺序特征提取。使用 PhysioNet 2016 CinC 数据集验证学习模型的准确性，然后使用我们收集的数据集表明，在数据集内纳入低频成分可以使 DL 模型的准确性提高 2%，灵敏度提高 4%。此外，LSTM 层比密集层的准确率高 0.8%。
