Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network_Journal of Biomedical Engineering

Authors：

WANG Qin ¹ , YANG Hongbo ^2,3 , PAN Jiahua ³ , TIAN Yingjie ¹ , GUO Tao ^2,3 ,  WANG Weilian ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China;
2. Kunming Medical University, Kunming 650500, P. R. China;
3. Fuwai Cardiovascular Hospital of Yunnan Province (Cardiovascular Hospital Affiliated to Kunming Medical University), Kunming 650102, P. R. China;

Corresponding author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

Classification of heart sounds; Fuzzy neural network; Power spectral density; Mel-frequency cepstral coefficients; Congenital heart disease

DOI：

10.7507/1001-5515.202301015

Video：

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Abstract Full text Figures/Tables Video References Cited by

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.

Citation： WANG Qin, YANG Hongbo, PAN Jiahua, TIAN Yingjie, GUO Tao, WANG Weilian. Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network. Journal of Biomedical Engineering, 2023, 40(6): 1152-1159. doi: 10.7507/1001-5515.202301015 Copy

1.	Zimmerman M S , Smith A G C , Sable C A , et al. Global, regional, and national burden of congenital heart disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Child Adolesc Health, 2020, 4(3): 185-200.
2.	Xu Weize, Yu Kai, Ye Jingjing, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artif Intell Med, 2022, 126: 102257.
3.	Al-Issa Y, Alqudah A M. A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep, 2022, 12(1): 14297.
4.	Springer D B, Tarassenko L, Clifford G D. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
5.	Sedighian P, Subudhi A W, Scalzo F, et al. Pediatric heart sound segmentation using Hidden Markov Model. Annu Int Conf IEEE Eng Med Biol Soc, 2014, 2014: 5490-5493.
6.	Milani M G M, Abas P E, De Silva L C, et al. Abnormal heart sound classification using phonocardiography signals. Smart Health, 2021, 21: 100194.
7.	Xiang Menghui, Zang Junbin, Wang Juliang, et al. Research of heart sound classification using two-dimensional features. Biomed Signal Process Control, 2023, 79: 104190.
8.	Roy A K, Misal A, Sinha G R. Classification of PCG signals: a survey. Int J Comput, 2014, 975: 8887.
9.	Asmare M H, Woldehanna F, Janssens L, et al. Rheumatic heart disease detection using deep learning from spectro-temporal representation of un-segmented heart sounds. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 168-171.
10.	Al-Naami B, Fraihat H, Gharaibeh N Y, et al. A Framework classification of heart sound signals in physionet challenge 2016 using high order statistics and adaptive neuro-fuzzy inference system. IEEE Access, 2020, 8: 224852-224859.
11.	赵玲, 李桥, 邵庆余, 等. 正常与异常心音信号比较研究. 中国医学物理学杂志, 2000(3): 154-156.
12.	张宇宁. 基于小波变换的心音信号降噪方法研究. 延吉: 延边大学, 2021.
13.	奎皓然, 潘家华, 宗容, 等. 基于持续时间隐马尔可夫模型的心音分割算法. 生物医学工程学杂志, 2020, 37(5): 765-774.
14.	张小兰, 房玉, 刘栋博, 等. 肥心病心音时频杂波特征提取识别算法研究. 电子测量与仪器学报, 2020, 34(4): 20-26.
15.	Nogueira D M, Ferreira C A, Gomes E F, et al. Classifying heart sounds using images of motifs, MFCC and temporal features. J Med Syst, 2019, 43(6): 168.
16.	许春冬, 辛鹏丽, 周静, 等. 基于功率谱密度与卷积神经网络的心音分类. 计算机工程与应用, 2021, 57(10): 8.
17.	Maity A, Mandal D, Misra I S, et al. A simple proposition for heart sound signal de-noising for effective components identification in normal and abnormal cases. Biomed Signal Process Control, 2022, 71(Pt B): 103264.
18.	Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw, 2020, 130: 22-32.
19.	Gudivada A A, Sudha G F. STQCA-FFT: A fast Fourier transform architecture using stack-type QCA approach with power and delay reduction. J Comput Sci, 2022, 60: 101594.
20.	Souza P V D C. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput, 2020, 92: 106275.
21.	Jang J S R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern Syst, 1993, 23(3): 665-685.
22.	Dhiman N, Sharma M K. Mediative Sugeno’s-TSK fuzzy logic based screening analysis to diagnosis of heart disease. Appl Math, 2019, 10(6): 448.
23.	Roy A K, Misal A. Performance evaluation of ANFIS for classification of PCG signal using wavelet transform. IJARECE, 2014, 3(9): 1034-1038.
24.	王幸之, 杨宏波, 宗容, 等. 基于子带包络和卷积神经网络的心音分类算法. 生物医学工程学杂志, 2021, 38(5): 969-978.

1. Zimmerman M S , Smith A G C , Sable C A , et al. Global, regional, and national burden of congenital heart disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Child Adolesc Health, 2020, 4(3): 185-200.
2. Xu Weize, Yu Kai, Ye Jingjing, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artif Intell Med, 2022, 126: 102257.
3. Al-Issa Y, Alqudah A M. A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep, 2022, 12(1): 14297.
4. Springer D B, Tarassenko L, Clifford G D. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
5. Sedighian P, Subudhi A W, Scalzo F, et al. Pediatric heart sound segmentation using Hidden Markov Model. Annu Int Conf IEEE Eng Med Biol Soc, 2014, 2014: 5490-5493.
6. Milani M G M, Abas P E, De Silva L C, et al. Abnormal heart sound classification using phonocardiography signals. Smart Health, 2021, 21: 100194.
7. Xiang Menghui, Zang Junbin, Wang Juliang, et al. Research of heart sound classification using two-dimensional features. Biomed Signal Process Control, 2023, 79: 104190.
8. Roy A K, Misal A, Sinha G R. Classification of PCG signals: a survey. Int J Comput, 2014, 975: 8887.
9. Asmare M H, Woldehanna F, Janssens L, et al. Rheumatic heart disease detection using deep learning from spectro-temporal representation of un-segmented heart sounds. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 168-171.
10. Al-Naami B, Fraihat H, Gharaibeh N Y, et al. A Framework classification of heart sound signals in physionet challenge 2016 using high order statistics and adaptive neuro-fuzzy inference system. IEEE Access, 2020, 8: 224852-224859.
11. 赵玲, 李桥, 邵庆余, 等. 正常与异常心音信号比较研究. 中国医学物理学杂志, 2000(3): 154-156.
12. 张宇宁. 基于小波变换的心音信号降噪方法研究. 延吉: 延边大学, 2021.
13. 奎皓然, 潘家华, 宗容, 等. 基于持续时间隐马尔可夫模型的心音分割算法. 生物医学工程学杂志, 2020, 37(5): 765-774.
14. 张小兰, 房玉, 刘栋博, 等. 肥心病心音时频杂波特征提取识别算法研究. 电子测量与仪器学报, 2020, 34(4): 20-26.
15. Nogueira D M, Ferreira C A, Gomes E F, et al. Classifying heart sounds using images of motifs, MFCC and temporal features. J Med Syst, 2019, 43(6): 168.
16. 许春冬, 辛鹏丽, 周静, 等. 基于功率谱密度与卷积神经网络的心音分类. 计算机工程与应用, 2021, 57(10): 8.
17. Maity A, Mandal D, Misra I S, et al. A simple proposition for heart sound signal de-noising for effective components identification in normal and abnormal cases. Biomed Signal Process Control, 2022, 71(Pt B): 103264.
18. Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw, 2020, 130: 22-32.
19. Gudivada A A, Sudha G F. STQCA-FFT: A fast Fourier transform architecture using stack-type QCA approach with power and delay reduction. J Comput Sci, 2022, 60: 101594.
20. Souza P V D C. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput, 2020, 92: 106275.
21. Jang J S R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern Syst, 1993, 23(3): 665-685.
22. Dhiman N, Sharma M K. Mediative Sugeno’s-TSK fuzzy logic based screening analysis to diagnosis of heart disease. Appl Math, 2019, 10(6): 448.
23. Roy A K, Misal A. Performance evaluation of ANFIS for classification of PCG signal using wavelet transform. IJARECE, 2014, 3(9): 1034-1038.
24. 王幸之, 杨宏波, 宗容, 等. 基于子带包络和卷积神经网络的心音分类算法. 生物医学工程学杂志, 2021, 38(5): 969-978.

Journal of Biomedical Engineering

Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network

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