基于瞬时频率特征的深度递归学习心音分割
Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features
摘要：
在这项工作中，提出了一种新的堆栈众所周知的技术，以确定一种自动的方法来分割心音图（PCG）中的心音。我们将展示一个深度递归神经网络（DRNN），它能够将PCG分割成其主要组成部分，以及一种非常具体的提取瞬时频率的方法，这将在所提出的模型的训练和测试中发挥重要作用。更具体地说，它涉及一个长短期记忆（LSTM）神经网络，伴随着傅立叶同步压缩变换（FSST），用于从PCG中提取瞬时时频特征。本方法在来自免费提供的数据库的长于5秒和短于35秒的心音信号上进行了测试。该方法证明，通过相对较小的架构、较小的数据集和正确的特征，该方法实现了几乎最先进的性能，显示出89.5%的平均灵敏度、89.3%的平均阳性预测值和91.3%的平均准确度。
英文摘要：
In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into its main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves a Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features from a PCG. The present approach was tested on heart sound signals longer than 5 seconds and shorter than 35 seconds from freely-available databases. This approach proved that, with a relatively small architecture, a small set of data, and the right features, this method achieved an almost state-of-the-art performance, showing an average sensitivity of 89.5%, an average positive predictive value of 89.3\% and an average accuracy of 91.3%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
