{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:24:47Z","timestamp":1776111887366,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Ukraine"},{"name":"British academy fellowship"},{"name":"H2020 project 101138678\u2014ZEBAI"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists\u2019 knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of acoustic sounds and heart murmurs using digital signal analysis. Using advanced machine learning algorithms, we aim to improve the accuracy, speed, and accessibility of heart murmur detection. The proposed method includes feature extraction from digital auscultatory recordings, preprocessing using signal processing techniques, and classification using state-of-the-art machine learning models. We evaluated the performance of different machine learning algorithms, such as convolutional neural networks (CNNs), random forests (RFs) and support vector machines (SVMs), on a selected heart noise dataset. The results show that our framework achieves high accuracy in differentiating normal heart sounds from different types of heart murmurs and provides a robust tool for clinical decision-making.<\/jats:p>","DOI":"10.3390\/computation12100208","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T03:49:34Z","timestamp":1729136974000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Classification of Acoustic Tones and Cardiac Murmurs Based on Digital Signal Analysis Leveraging Machine Learning Methods"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-8534","authenticated-orcid":false,"given":"Nataliya","family":"Shakhovska","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Zagorodniy","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/83.799889","article-title":"WaveGuide: A joint wavelet-based image representation and description system","volume":"8","author":"Liang","year":"1999","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s13246-020-00897-w","article-title":"Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals","volume":"43","author":"Saeedi","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.2174\/0929866511009011473","article-title":"Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition","volume":"17","author":"Pugalenthi","year":"2010","journal-title":"Protein Pept. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"055006","DOI":"10.1088\/1361-6579\/ab8770","article-title":"Automatic heart sound classification from segmented\/unsegmented phonocardiogram signals using time and frequency features","volume":"41","author":"Khan","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_5","unstructured":"Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., and Sricharan, K. (2017). Recognizing abnormal heart sounds using deep learning. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Roy, T.S., Roy, J.K., and Mandal, N. (2024). Improvement in the performance of deep learning based on CNN to classify the heart sound by evaluating hyper-parameters. Soft Computing, Springer.","DOI":"10.1007\/s00500-024-09909-3"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, B., Zhou, Z., Yu, S., Liang, X., Xie, Y., and Sun, Q. (2024). Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet\/CinC Challenge 2016 Database. Electronics, 13.","DOI":"10.3390\/electronics13163222"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image matching from handcrafted to deep features: A survey","volume":"129","author":"Ma","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124620","DOI":"10.1016\/j.eswa.2024.124620","article-title":"JL-TFMSFNet: A domestic cat sound emotion recognition method based on jointly learning the time\u2013frequency domain and multi-scale features","volume":"255","author":"Tang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vinay, N.A., Vidyasagar, K.N., Rohith, S., Dayananda, P., Supreeth, S., and Bharathi, S.H. (2024). An RNN-Bi LSTM based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) from Heart Beat Sound: A Feature Optimization Process. IEEE Access, IEEE.","DOI":"10.1109\/ACCESS.2024.3397574"},{"key":"ref_11","unstructured":"(2024, August 10). PhysioNet\/CinC Challenge 2016 Database. Available online: https:\/\/archive.physionet.org\/pn3\/challenge\/2016\/."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"eGeneral Medical Heart Sound Database, Liu, C., Springer, D., Li, Q., Moody, B., Juan, R.A., Chorro, F.J., Castells, F., Roig, J.M., and Silva, I. (2016). An open access database for the evaluation of heart sound algorithms. Physiol Meas., 37, 2181\u20132213.","DOI":"10.1088\/0967-3334\/37\/12\/2181"},{"key":"ref_13","unstructured":"(2024, August 10). Heart Sound Dataset from Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/kinguistics\/heartbeat-sounds\/."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kharitonov, E., Rivi\u00e8re, M., Synnaeve, G., Wolf, L., Mazar\u00e9, P.E., Douze, M., and Dupoux, E. (2021, January 19\u201322). Data augmenting contrastive learning of speech representations in the time domain. Proceedings of the 2021 IEEE Spoken Language Technology Workshop (SLT), Shenzhen, China.","DOI":"10.1109\/SLT48900.2021.9383605"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/TNSRE.2024.3419013","article-title":"Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI","volume":"32","author":"Huang","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Long, Y., Zhang, Q., Zeng, B., Gao, L., Liu, X., Zhang, J., and Song, J. (2022). Frequency domain model augmentation for adversarial attack. European Conference on Computer Vision, Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-19772-7_32"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"070502","DOI":"10.1103\/PhysRevLett.108.070502","article-title":"Efficient method for computing the maximum-likelihood quantum state from measurements with additive gaussian noise","volume":"108","author":"Smolin","year":"2012","journal-title":"Phys. Rev. Lett."},{"key":"ref_18","unstructured":"Arnault, A., Hanssens, B., and Riche, N. (2020). Urban Sound Classification: Striving towards a fair comparison. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Safara, F., Doraisamy, S., Azman, A., Jantan, A., and Ranga, S. (2012). Wavelet packet entropy for heart murmurs classification. Adv. Bioinform., 2012.","DOI":"10.1155\/2012\/327269"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Daud, S.S., and Sudirman, R. (2015, January 9\u201312). Butterworth bandpass and stationary wavelet transform filter comparison for electroencephalography signal. Proceedings of the 2015 6th International Conference on Intelligent Systems, Modelling and Simulation, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISMS.2015.29"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1097\/JCE.0000000000000335","article-title":"Classification system for heart sounds based on random forests","volume":"44","author":"Esmail","year":"2019","journal-title":"J. Clin. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cmpb.2009.01.003","article-title":"Support vectors machine-based identification of heart valve diseases using heart sounds","volume":"95","author":"Maglogiannis","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Singh, S.A., Majumder, S., and Mishra, M. (2019, January 20\u201323). Classification of short unsegmented heart sound based on deep learning. Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand.","DOI":"10.1109\/I2MTC.2019.8826991"},{"key":"ref_27","first-page":"393","article-title":"A Novel Person Authentication Technique Using Electrocardiogram (ECG)","volume":"20","author":"Murthy","year":"2024","journal-title":"J. Electr. Syst."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/10\/208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:56Z","timestamp":1760112896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/10\/208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["computation12100208"],"URL":"https:\/\/doi.org\/10.3390\/computation12100208","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,17]]}}}