{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:51:28Z","timestamp":1778255488606,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the frequency domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1\/10 of the size of the state-of-the-art work.<\/jats:p>","DOI":"10.3390\/info12020054","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:38:16Z","timestamp":1611621496000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4985-3122","authenticated-orcid":false,"given":"Yibo","family":"Yin","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1379-591X","authenticated-orcid":false,"given":"Kainan","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sitao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"ref_1","unstructured":"(2021, January 25). WHO 2015 World Statistics on Cardiovascular Disease. Available online: www.who.int\/mediacentre\/factsheets\/fs317\/en\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1088\/0967-3334\/37\/12\/2181","article-title":"An open access database for the evaluation of heart sound algorithms","volume":"37","author":"Liu","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_3","first-page":"261","article-title":"Finding Disease Similarity by Combining ECG with Heart Auscultation Sound","volume":"34","author":"Wang","year":"2007","journal-title":"Comput. Cardiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S0033-0620(63)80007-9","article-title":"Computer analysis of phonocardiograms","volume":"5","author":"Gerbarg","year":"1963","journal-title":"Prog. Cardiovasc. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, H., Dai, Z., Jiang, Y., Li, T., and Liu, C. (2018). PCG Classification Using Mulidomain Features and SVM Classifier. BioMed Res. Int., 2018.","DOI":"10.1155\/2018\/4205027"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Son, G.-Y., and Kwon, S. (2018). Classification of Heart Sound Signal Using Multiple Features. Appl. Sci., 8.","DOI":"10.3390\/app8122344"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2018.09.101","article-title":"Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption","volume":"392","author":"Xiao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Noman, F., Ting, C.M., Salleh, S.H., and Ombao, H. (2019, January 12\u201317). Short-segment heart sound classification using an ensemble of deep convolutional neural networks. Proceedings of the 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682668"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13634-019-0651-3","article-title":"Feature extraction and classification of heart sound using 1D convolutional neural networks","volume":"2019","author":"Li","year":"2019","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neunet.2020.06.015","article-title":"Heart sound classification based on improved MFCC features and convolutional recurrent neural networks","volume":"130","author":"Deng","year":"2020","journal-title":"Neural Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.compbiomed.2018.06.026","article-title":"A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection","volume":"100","author":"Bozkurt","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1088\/1361-6579\/aa724c","article-title":"EHeart sound classification from unsegmented phonocardiograms","volume":"38","author":"Langley","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lile, France."},{"key":"ref_15","unstructured":"(2020, January 25). Michigan Heart Sounds and Murmur Database. Available online: www.med.umich.edu\/lrc\/psb\/heartsounds\/index.html."},{"key":"ref_16","unstructured":"(2020, January 25). Classifying Heart Sounds Challenge. Available online: www.peterjbentley.com\/heartchallenge."},{"key":"ref_17","unstructured":"(2020, January 25). Heart Auscultation Heart Murmur Database. Available online: http:\/\/www.egeneralmedical.com\/listohearmur.html."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","article-title":"Approximation Capabilities of Multilayer Feedforward Networks","volume":"4","author":"Hornik","year":"1991","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, F., Tang, H., Shang, S., Mathiak, K., and Cong, F. (2020). Classification of Heart Sounds Using Convolutional Neural Network. Appl. Sci., 10.","DOI":"10.3390\/app10113956"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nilanon, T., Yao, J., Hao, J., Purushotham, S., and Liu, Y. (2016, January 11\u201314). Normal\/Abnormal Heart Sound Recordings Classification Using Convolutional Neural Network. Proceedings of the 43rd Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.169-535"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., and Sricharan, K. (2016, January 11\u201314). Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients. Proceedings of the 43rd Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.236-175"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Potes, C., Parvaneh, S., Rahman, A., and Conroy, B. (2016, January 11\u201314). Ensemble of Feature-based and Deep learning-based Classifiers for Detection of Abnormal Heart Sounds. Proceedings of the 43rd Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.182-399"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:15:17Z","timestamp":1760159717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/2\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,26]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["info12020054"],"URL":"https:\/\/doi.org\/10.3390\/info12020054","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,26]]}}}