scholarly journals Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

2019 ◽  
Vol 25 (3) ◽  
pp. 71-76 ◽  
Author(s):  
Grega Vrbancic ◽  
Iztok Jr. Fister ◽  
Vili Podgorelec
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Domain Knowledge ◽  
Heart Sound ◽  
Automatic Segmentation ◽  
Poor Performance ◽  
Deep Convolutional Neural Networks ◽  
Signal Segmentation ◽  
Variable Environments ◽  
Stationary Signals

The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and does not require any complex pre-processing. The proposed method was tested on a set of heartbeat sound clips, collected by non-experts with mobile devices in highly variable environments with excessive background noise. The obtained results show that the proposed method outperforms other methods, which are taking advantage of using domain knowledge for the analysis of the signals. Based on the encouraging experimental results, we believe that the proposed method can be considered as a solid basis for the further development of the automatic segmentation of highly variable signals using deep neural networks.

Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA77-WA86 ◽  
Author(s):  
Haibin Di ◽  
Zhun Li ◽  
Hiren Maniar ◽  
Aria Abubakar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Seismic Data ◽  
Network Architecture ◽  
Domain Knowledge ◽  
Model Building ◽  
Seismic Stratigraphy ◽  
Training Data ◽  
Data Sets ◽  
Deep Convolutional Neural Networks

Depicting geologic sequences from 3D seismic surveying is of significant value to subsurface reservoir exploration, but it is usually time- and labor-intensive for manual interpretation by experienced seismic interpreters. We have developed a semisupervised workflow for efficient seismic stratigraphy interpretation by using the state-of-the-art deep convolutional neural networks (CNNs). Specifically, the workflow consists of two components: (1) seismic feature self-learning (SFSL) and (2) stratigraphy model building (SMB), each of which is formulated as a deep CNN. Whereas the SMB is supervised by knowledge from domain experts and the associated CNN uses a similar network architecture typically used in image segmentation, the SFSL is designed as an unsupervised process and thus can be performed backstage while an expert prepares the training labels for the SMB CNN. Compared with conventional approaches, the our workflow is superior in two aspects. First, the SMB CNN, initialized by the SFSL CNN, successfully inherits the prior knowledge of the seismic features in the target seismic data. Therefore, it becomes feasible for completing the supervised training of the SMB CNN more efficiently using only a small amount of training data, for example, less than 0.1% of the available seismic data as demonstrated in this paper. Second, for the convenience of seismic experts in translating their domain knowledge into training labels, our workflow is designed to be applicable to three scenarios, trace-wise, paintbrushing, and full-sectional annotation. The performance of the new workflow is well-verified through application to three real seismic data sets. We conclude that the new workflow is not only capable of providing robust stratigraphy interpretation for a given seismic volume, but it also holds great potential for other problems in seismic data analysis.

Phonocardiogram Classification Using Deep Convolutional Neural Networks with Majority Vote Strategy

2019 ◽  
Vol 9 (8) ◽  
pp. 1692-1704
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Jue Wang ◽  
Huiqun Wu ◽  
Hui Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Heart Sound ◽  
Majority Vote ◽  
Heart Sounds ◽  
Cardiac Diseases ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Vote Strategy ◽  
Pcg Signals

Most current automated phonocardiogram (PCG) classification methods are relied on PCG segmentation. It is universal to make use of the segmented PCG signals and then extract efficiency features for computer-aided auscultation or heart sound classification. However, the accurate segmentation of the fundamental heart sounds depends greatly on the quality of the heart sound signals. In addition these methods that heavily relied on segmentation algorithm considerably increase the computational burden. To solve above two issues, we have developed a novel approach to classify normal and abnormal cardiac diseases with un-segmented PCG signals. A deep Convolutional Neural Networks (DCNNs) method is proposed for recognizing normal and abnormal cardiac diseases. In the proposed method, one-dimensional heart sound signals are first converted into twodimensional feature maps which have three channels and each of them represents Mel-frequency spectral coefficients (MFSC) features including static, delta and delta–delta. These artificial images are then fed to the proposed DCNNs to train and evaluate normal and abnormal heart sound signals. We combined the method of majority vote strategy to finally obtain the category of PCG signals. Sensitivity (Se), Specificity (Sp) and Mean accuracy (MAcc) are used as the evaluation metrics. Results: Experiments demonstrated that our approach achieved a significant improvement, with the high Se, Sp, and MAcc of 92.73%, 96.90% and 94.81% respectively. The proposed method improves the MAcc by 5.63% compared with the best result in the CinC Challenge 2016. In addition, it has better robustness performance when applying for the long heart sounds. The proposed DCNNs-based method can achieve the best accuracy performance on recognizing normal and abnormal heart sounds without the preprocessing of segmental algorithm. It significantly improves the classification performance compared with the current state-of-art algorithm.

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