Identification of the Normal and Abnormal Heart Sounds Based on Energy Features and Neural Network

2017 ◽  
pp. 554-561
Author(s):  
Ting Li ◽  
Hong Tang ◽  
Xiao-ke Xu
Keyword(s):  
Neural Network ◽  
Heart Sounds

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

scholarly journals A novel 1-D densely connected feature selection convolutional neural network for heart sounds classification

2021 ◽  
Vol 9 (24) ◽  
pp. 1752-1752
Author(s):  
Xin Zhou ◽  
Xuying Wang ◽  
Xianhong Li ◽  
Yao Zhang ◽  
Ying Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Heart Sounds

Neural network classification of homomorphic segmented heart sounds

2007 ◽  
Vol 7 (1) ◽  
pp. 286-297 ◽  
Author(s):  
Cota Navin Gupta ◽  
Ramaswamy Palaniappan ◽  
Sundaram Swaminathan ◽  
Shankar M. Krishnan
Keyword(s):  
Neural Network ◽  
Heart Sounds ◽  
Neural Network Classification

scholarly journals Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

2020 ◽  
Author(s):  
Chun Dong Xu ◽  
Jing Zhou ◽  
Dong Wen Ying ◽  
Lei Jing Hou ◽  
Qing Hua Long
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Heart Sound ◽  
Gaussian Mixture ◽  
Heart Sounds ◽  
Spectral Amplitude ◽  
Heart Sound Segmentation ◽  
F Measure

Abstract Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

scholarly journals Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

2020 ◽  
Author(s):  
Chun Dong Xu ◽  
Jing Zhou ◽  
Dong Wen Ying ◽  
Lei Jing Hou ◽  
Qing Hua Long
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Heart Sound ◽  
Gaussian Mixture ◽  
Heart Sounds ◽  
Spectral Amplitude ◽  
Heart Sound Segmentation ◽  
F Measure

Abstract Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

Sign in / Sign up

Export Citation Format

Share Document