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心音心电信号处理的神经网络方法_毕业论文.pdf
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心音心电信号处理的神经网络方法_毕业论文.pdf
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四川大学博士研究生学位论文
以验证其是否有效。此外,新的心音标注任务需要新的时序特性表达,需重新构建和设计
特征提取算法。本文提出基于卷积网络和长短时记忆网络的端到端心音分段算法,直接利
用原始音频信号实现心音分段,该算法简化了心音信号处理步骤,提高了心音信号分段在
不同数据上的适应性,并赋予算法多任务能力,通过在真实公开数据集上与基准算法的比
较,证明了该算法的性能。
3. 研究了心音信号的分类任务,提 出基于多尺度时序检测的神经网络分类算法,将
心音分类问题转化为心脏杂音标注问题,实现了面向单个预测的局部可解释性。 现有神
经网络心音分类算法将心音信号作视为一个整体,可预测心音样本类别但无法获取医学
可解释性信息。因此,本研究将心音信号分类问题转化为对心脏杂音的时序标注问题,以
解决神经网络方法在临床应用中的可解释性难题。 为进一步提高在心音时序中对心脏杂
音的检测能力,提出了一种基于多尺度时序检测的神经网络心音分类算法,通过在基准
数据集上的实验证明,该算法具有更好的分类性能,并在时序标注性能是优于其他算法。
本文将心音分段任务及心音分类任务集成于同一个算法, 并通过获取心脏杂音位置信息,
实现心音信号分类的局部可解释性。
4. 研究了无创胎儿心电图的胎儿R峰检测任务,首次通过神经网络序列标注算法直接
在腹部心电信号中标注和检测胎儿R峰,提出了基于记忆 门控编码及时序解码的神经网络
序列标注算法,该算法简化了R峰检测处理流程 并有更好的时序标注性能。 现有胎儿R峰
检测算法需首先从孕妇腹部心电信号中剔除母亲心电信号成分,本文将 胎儿R峰检测过程
建模为序列标注任务,直接在孕妇腹部心电信号上检测R峰位置。为进一步提高检测准确
率,本文提出了一种基于记忆门控编码及时序解码算法和训练策略,基准数据集实验证明
该算法与其他序列算法相比有更好的性能。本文针对标签序列中类别不平衡问题,改进了
算法损失函数以获取更好的标注性能及更快的收敛速度。此外,本文测试多种目标标签编
码策略以评估算法多任务能力,并对信号通道数量等影响性能的因素进行了研究。
利用以上研究成果,本文设计开发了“基于物联网的心音信号自动分析系统”
1
。 该
系统通过物联网采集心音信号数据,实现了心音质量评价,心脏循环和心脏储备功能量化
测评,心脏杂音时序检测及心音信号分类等功能。
关键词:神经网络,序列标注,长短时记忆网络,心音信号,心电信号
1
http://116.62.119.13:8000/index1/
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ABSTRACT
Abstract
Phonocardiogram (PCG) and electrocardiogram (ECG) have the characteristics of easy col-
lection, low price, noninvasive, repeatability, and easy operation in the medical field, which is used
for disease prevention, preliminary diagnosis, and long-term monitoring. Therefore, researcher-
s and medical workers pay more and more attention to these signals, and the demand for PCG
and ECG processing and analysis technology is also increasing. Efficient and accurate process-
ing of these medical sequence signals can reduce the pressure of doctors in clinical diagnosis. In
addition, long-term monitoring of patients using these signals can help doctors design disease pre-
vention and control programs, thereby improving the overall health of society as a whole. Through
decades of research, the analysis and application of PCG and ECG have made great progress, but
the traditional signal analysis methods are still facing challenges in the processing of these se-
quence signals. Using the neural network method to effectively analyze and model the PCG and
ECG has become a research hot spot in the medical field.
In this dissertation, four neural network methods are proposed and applied to heart sound seg-
mentation, detecting abnormal heart sound. The main content and contributions of this dissertation
are listed as follows.
1. In the analysis of heart sound segmentation, the task is described as a sequence
annotation task. To address the challenge of context-sensitive heart sound segmentation,
a global structure information-based model for heart sound segmentation is proposed to
improve the accuracy of location prediction. Heart sound segmentation, which aims at detecting
the first and second heart sound in phonocardiogram, is an essential step to automatically analyze
heart valve diseases. Recently, neural network-based methods have demonstrated their promising
performance in segmenting the heart sound data. However, the methods also suffer from serious
limitations due to the used envelope features. The reason is largely due to that the envelope features
cannot effectively model the intrinsic sequential characteristic, resulting in the poor utilization
of the duration information of heart cycles. In this section, we propose a Duration Long-Short
Term Memory network (Duration LSTM) to effectively address this problem by incorporating
the duration features. The proposed method is investigated in the real-world phonocardiogram
dataset (Massachusetts Institute of Technology heart sounds database) and compared with the two
representatives of the existing state-of-the-art methods, the experimental results demonstrate that
the proposed method has a promising performance on different tolerance windows. In addition, the
proposed model also has some advantages in the impact of recording length and the phenomenon
of the end effect.
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四川大学博士研究生学位论文
2. In the research on feature extraction of PCG, an end-to-end algorithm is proposed
to address the HSS tasks, which can directly use the original digital audio signal as input.
Experimental comparisons have demonstrated the competitive performance of the proposed
algorithm against the state of the arts. The traditional heart sound segmentation methods need
to manually extract the features before dealing with PCG signal. These artificial features highly
rely on extraction algorithms, which often result in poor performance due to the different oper-
ating environments. In addition, the high-dimension and frequency characteristics of audio also
challenge the traditional methods in effectively addressing the HSS tasks. This paper presents a
novel end-to-end method based on the Convolutional Long Short-term Memory (CLSTM), which
directly uses the audio recording as input to address the segmentation tasks. Particularly, the con-
volutional layers are designed to extract the meaningful features and perform the downsampling,
and the LSTM layers are developed to conduct the sequence recognition, both components col-
lectively improve the robustness and adaptability in processing the HSS tasks. Furthermore, the
proposed CLSTM algorithm is easily extended to other complex heart sound annotation tasks, as
does not need to extract the characteristics of corresponding tasks in advance. Besides, the pro-
posed algorithm can also be regarded as a powerful feature extraction tool, which can be integrated
into the existing models for heart sound segmentation. Experimental results on real-world PCG
datasets, through comparisons to peer competitors, demonstrate the outstanding performance of
the proposed algorithm.
3. In the research on PCG classification tasks, an algorithm is proposed for anomaly
(normal vs. abnormal) detection of PCG recording, and the proposed method can deter-
mine the location of heart sound signal in classification, which provides the basis for the in-
terpretability and visualization of heart sound signal classification. Existing neural networks-
based methods for PCG classification tasks only provide the category information and cannot
obtain the location of heart murmurs. In this section, we model the heart sound classification
problem as a sequence tagging task and then provide an automatic classification algorithm for
anomaly (normal vs. abnormal) detection of PCG recording. The algorithm can determine the lo-
cation of heart sound signal in classification, which provides the basis for the interpretability and
visualization of heart sound signal classification. In the detection phase, the abnormal heart sound
is determined when the number of heart murmurs in the state prediction sequence exceeds a certain
threshold instantaneously. PhysioNet/CinC Challenge heart sound database is used for evaluation
and the synthetic minority over-sampling technique method is applied to balance the datasets. By
using the 5-fold cross-validation style, experimental results demonstrate that the proposed method
has a comparative performance and generalization ability than other tagging methods. The re-
sults verify the effectiveness of the proposed method which can serve as a potential candidate for
IV
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ABSTRACT
automatic anomaly detection in the clinical application.
4. In the research on the non-invasive fetal electrocardiography, an algorithm is pro-
posed to detect R-peak series by assigning a categorical label to each member of the observed
values in ECG sequence, the proposed method has a comparative performance and general-
ization ability than other tagging methods. The non-invasive fetal electrocardiography method
is widely used for monitoring the heartbeat of the fetus during pregnancy, largely owing to its
low cost, easy operation, and continuous monitoring. In non-invasive fetal ECG, it is necessary to
detect the R-peak series from the abdominal electrode signal, which is an indication and basis for
obtaining the fetal heart rate. Traditional methods of fetal R-peak determination require to elimi-
nation maternal components from abdominal ECG signals, which need to satisfy the assumption of
linear stability and require high computability. This section explores directly detecting R-peak se-
ries by assigning a categorical label to each member of the observed values in ECG sequence, and
proposes a convolutional encoder-decoder network and training strategy to process the sequence
annotation task. Specifically, the encoder is a stacked convolutional layer equipped with Gating
Linear Unit (GLU), and the decoder is a recurrent neural network. The GLU convolutional layer
can effectively extract and aggregate the features to improve generalization ability. To counter the
impact of the unbalanced sequence label, the focal loss function is adopted and adjusted to achieve
better prediction performance yet with faster convergence. The experimental results show that the
proposed method can achieve promising performance on the benchmark dataset. In addition, the
flexibility of the proposed method is demonstrated by testing different label encoding strategies,
and it can be used for other complicated fetal ECG labeling tasks.
Keywords: neural networks, sequence labeling, long short-term memory networks, phonocardio-
gram, electrocardiogram
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目录
目录
摘要 ................................................................................................................... I
Abstract ............................................................................................................. III
目录 ................................................................................................................... VII
插图 ................................................................................................................... XI
表格 ................................................................................................................... XII
第1章 绪论 ........................................................................................................ 1
1.1 研究背景与意义 ...................................................................................... 1
1.1.1 心音信号研究背景及意义 ............................................................... 1
1.1.2 胎儿心电信号研究背景及意义......................................................... 2
1.2 研究现状 ................................................................................................ 4
1.2.1 心音信号算法概述 ......................................................................... 4
1.2.2 胎儿心电信号算法概述 .................................................................. 8
1.3 本文的创新点与主要贡献 ......................................................................... 9
1.3.1 基于全局结构信息的神经网络序列标注算法 ..................................... 9
1.3.2 基于端到端时序特征表达的神经网络序列标注算法 ........................... 10
1.3.3 基于多尺度时序检测的神经网络分类算法 ........................................ 10
1.3.4 基于记忆门控编码及时序解码的神经网络序列标注算法..................... 11
1.3.5 基于物联网的心音信号自动分析系统............................................... 11
1.4 论文组织结构.......................................................................................... 12
第2章 基于全局结构信息的神经网络序列标注算法................................................. 15
2.1 引言....................................................................................................... 15
2.2 研究动机 ................................................................................................ 17
2.3 算法基础 ................................................................................................ 19
2.3.1 心音包络特征 ............................................................................... 19
2.3.2 长短时记忆网络 ............................................................................ 20
2.4 全局结构信息心音分段网络 ...................................................................... 23
2.4.1 网络结构 ...................................................................................... 23
2.4.2 网络训练 ...................................................................................... 25
2.4.3 实现细节 ...................................................................................... 26
2.5 实验的结果及分析 ................................................................................... 28
2.5.1 基准数据集................................................................................... 28
2.5.2 评价指标 ...................................................................................... 29
VII
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