Feature extraction and classification of heart sound using 1D convolutional neural networks

Li, Fen; Liu, Ming; Zhao, Yuejin; Kong, Lingqin; Dong, Liquan; Liu, Xiaohua; Hui, Mei

doi:10.1186/s13634-019-0651-3

Cited by 93 publications

(56 citation statements)

References 25 publications

(17 reference statements)

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“…The proposed CC 1D-CNN with three or four convolution layers (CPCNN4 or CPCNN5, respectively), denoising autoencoder (DAE) network (DAENet) [ 29 ], and GammatoneNet [ 19 ] are all 1D-CNNs, which learn the representation directly from the signal. Heart sounds were extracted using the DAE algorithm and used as the input feature of the DAENet.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Zhao

Bao

et al. 2021

Sensors

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When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

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Section: Experiments and Resultsmentioning

confidence: 99%

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Zhao

Bao

et al. 2021

Sensors

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show abstract

“…The proposed chest compression CNN with 4 or 5 convolution layers (CPCNN4 or CPCNN5), the DAENet [25], the GammatoneNet [19] are 1D-CNN, which learn the representation directly from the signals. Heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D-CNN of DAENet.…”

Section: Comparison Of Different Methodsmentioning

confidence: 99%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

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“…Convolutional neural networks (CNN) are known for their outstanding performance on image (2D data) related machine learning tasks, such as image segmentation, object recognition, and super resolution. A modified version of CNN, known as 1D CNN [30,53], has been developed for time series and other 1D data (sound, vibration, sentences, etc.). CNN layers as shown in Figure 4a are comprised of filters that capture the local correlation of nearby data points, instead of the conventional full connection.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Kumar

Tzou

2020

Energies

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Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

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Feature extraction and classification of heart sound using 1D convolutional neural networks

Cited by 93 publications

References 25 publications

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

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