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首页> 外文会议>International Conference on Biomedical and Health Informatics >Spectrogram and Deep Neural Network Analysis in Detecting Paroxysmal Atrial fibrillation with Bottleneck Layers and Cross Entropy Approach
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Spectrogram and Deep Neural Network Analysis in Detecting Paroxysmal Atrial fibrillation with Bottleneck Layers and Cross Entropy Approach

机译：用瓶颈层检测阵发性心房颤动的谱图和深神经网络分析及交叉熵方法

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Paroxysmal AF (PAF) is a form of atrial fibrillation (AF) that is generally clinically silent and undetected. AF is a type of heart disease called cardiac arrhythmia. Automatic detection of AF could make a significant contribution to early diagnosis, control and prevention of chronic AF complications. In this paper, authors presented a novel algorithm through spectrogram and deep learning neural network analysis in detecting paroxysmal AF from image data segments. This method does not require me detection of P and/or R peaks which is a preprocessing step required by many existing algorithms. The PAF Prediction Challenge Database from Physionetorg were used as learning set which composed of 50 record sets. These records were converted into 7,000 PAF and 964 healthy data segments. Each data segment has 5 mins-duration and converted it to graph images. These graph images are then converted into spectrogram to visualize the frequency band present in the spectrum. In this process, ECG numerical values were interpreted into spectrogram form. Spectrogram images are cropped to remove unnecessary markings from the graphing and spectrogram processes. Cropped spectrogram images are then grouped into separate folders according to type. The produced datasets are then fed into training using 500,000 training steps. The algorithm is integrated with Ten-sorFlow CPU version 1.5 and Inception V3 model to take advantage of its astonishing way on how it analyzes images. The deep learning neural network involves a bottleneck layer which uses lesser neurons to reduce the number of feature maps in the network to get the best loss during training. In order to have a faster learning rate, the cross-entropy cost function was used. The final accuracy test from the training reached as high as 96.8%. An actual test for identified PAF and healthy datasets from Physionetorg were performed and all are correctly predicted and thus could be able to classify other different diseases based from converted ECG numerical values. Furthermore, this paper established a low-powered workstation's requirement for implementation because it only requires at least a dual core processor and 2 GB of RAM.
机译：阵发性AF（PAF）是一种心房颤动（AF）的形式，其通常是临床沉默和未被发现的。 AF是一种称为心律失常的心脏病。自动检测AF可以对早期诊断，控制和预防慢性AF并发症作出重大贡献。在本文中，作者通过频谱图和深入学习神经网络分析来介绍一种在图像数据段检测阵发性AF中的深度学习神经网络分析。该方法不需要我检测P和/或R峰，这是许多现有算法所需的预处理步骤。 Physoonetorg的PAF预测挑战数据库被用作学习集，由50个记录集组成。这些记录转换为7,000个PAF和964个健康数据段。每个数据段都有5分钟持续时间并将其转换为图形图像。然后将这些图形图像转换为频谱图以可视化频谱中存在的频带。在该过程中，ECG数值被解释为谱图形式。裁剪谱图图像以从图形和频谱图过程中消除不必要的标记。然后根据类型将裁剪谱图图像分组成单独的文件夹。然后使用500,000次训练步骤馈送生产的数据集。该算法与十SORFLOW CPU版本1.5和Inception V3模型集成，以利用其对图像的令人惊讶的方式。深度学习神经网络涉及瓶颈层，它使用较小的神经元来减少网络中的特征贴图的数量，以获得训练期间的最佳损失。为了具有更快的学习率，使用跨熵成本函数。培训的最终准确性测试达到高达96.8％。执行了来自PhysooneTorg的识别PAF和健康数据集的实际测试，并且所有都是正确预测的，因此可以能够将基于转换的ECG数值的其他不同疾病进行分类。此外，本文建立了低功耗的工作站的实现要求，因为它只需要至少一个双核处理器和2 GB的RAM。

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