{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:06:11Z","timestamp":1782317171119,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health","award":["CUP F83C22001380006"],"award-info":[{"award-number":["CUP F83C22001380006"]}]},{"name":"\u201cSistema di Monitoraggio ed Analisi basato su intelligenza aRTificiale per pazienti affetti da scompenso CARdiaco cronico con dispositivi medici miniinvasivi e indossabili Evoluti\u2014SMART CARE\u201d","award":["CUP F83C22001380006"],"award-info":[{"award-number":["CUP F83C22001380006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary (\u201cNormal\u201d\/\u201dPathologic\u201d) and multiclass (\u201cNormal\u201d, \u201cCAD\u201d (coronary artery disease), \u201cMVP\u201d (mitral valve prolapse), and \u201cBenign\u201d (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet\/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers\u2019 performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.<\/jats:p>","DOI":"10.3390\/s24123853","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T08:02:26Z","timestamp":1718352146000},"page":"3853","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0893-138X","authenticated-orcid":false,"given":"Roberto","family":"De Fazio","sequence":"first","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6847-8673","authenticated-orcid":false,"given":"Lorenzo","family":"Spongano","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"De Vittorio","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8591-1190","authenticated-orcid":false,"given":"Luigi","family":"Patrono","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4058-4042","authenticated-orcid":false,"given":"Paolo","family":"Visconti","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Center for Biomolecular Nanotechnologies, Italian Institute of Technology, 73010 Arnesano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sergi, I., Montanaro, T., Shumba, A.T., De Fazio, R., Visconti, P., and Patrono, L. (2023, January 20\u201323). An IoT-Based Platform for Remote Monitoring of Patients with Heart Failure: An Overview of Integrable Devices. Proceedings of the 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech), Split\/Bol, Croatia.","DOI":"10.23919\/SpliTech58164.2023.10193686"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Boulares, M., Alotaibi, R., AlMansour, A., and Barnawi, A. (2021). Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182010952"},{"key":"ref_3","first-page":"806","article-title":"Systematic Review for Phonocardiography Classification Based on Machine Learning","volume":"14","author":"Altaf","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., and Xu, C. (2021). Deep Learning Methods for Heart Sounds Classification: A Systematic Review. Entropy, 23.","DOI":"10.3390\/e23060667"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1080\/03091902.2020.1799095","article-title":"Analysis of the Four Heart Sounds Statistical Study and Spectro-Temporal Characteristics","volume":"44","author":"Debbal","year":"2020","journal-title":"J. Med. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Al-Naami, B., Fraihat, H., Al-Nabulsi, J., Gharaibeh, N.Y., Visconti, P., and Al-Hinnawi, A.-R. (2022). Assessment of Dual-Tree Complex Wavelet Transform to Improve SNR in Collaboration with Neuro-Fuzzy System for Heart-Sound Identification. Electronics, 11.","DOI":"10.3390\/electronics11060938"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Al-Naami, B., Fraihat, H., Owida, H.A., Al-Hamad, K., De Fazio, R., and Visconti, P. (2022). Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS. Computers, 11.","DOI":"10.3390\/computers11060093"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Langley, P., and Murray, A. (2016, January 11\u201314). Abnormal Heart Sounds Detected from Short Duration Unsegmented Phonocardiograms by Wavelet Entropy. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.156-268"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1088\/1361-6579\/aa724c","article-title":"Heart Sound Classification from Unsegmented Phonocardiograms","volume":"38","author":"Langley","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s13246-020-00851-w","article-title":"Automated Heart Sound Classification System from Unsegmented Phonocardiogram (PCG) Using Deep Neural Network","volume":"43","author":"Krishnan","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Singh-Miller, N., and Singh-Miller, N. (2016, January 11\u201314). Using Spectral Acoustic Features to Identify Abnormal Heart Sounds. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.160-401"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Goda, M.A., and Hajas, P. (2016, January 11\u201314). Morphological Determination of Pathological PCG Signals by Time and Frequency Domain Analysis. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.324-249"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tang, H., Dai, Z., Jiang, Y., Li, T., and Liu, C. (2018). PCG Classification Using Multidomain Features and SVM Classifier. BioMed Res. Int., 2018.","DOI":"10.1155\/2018\/4205027"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nilanon, T., Purushotham, S., and Liu, Y. (2016, January 11\u201314). Normal\/Abnormal Heart Sound Recordings Classification Using Convolutional Neural Network. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.169-535"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1088\/1361-6579\/aa7982","article-title":"Ensemble Methods with Outliers for Phonocardiogram Classification","volume":"38","author":"Warrick","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1088\/1361-6579\/aa6a3d","article-title":"DropConnected Neural Networks Trained on Time-Frequency and Inter-Beat Features for Classifying Heart Sounds","volume":"38","author":"Kay","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_17","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 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.182-399"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, T., Li, P., Liu, Y., Zhang, H., Li, Y., Jiao, Y., Liu, C., Karmakar, C., Liang, X., and Ren, M. (2021). Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals. Entropy, 23.","DOI":"10.3390\/e23060642"},{"key":"ref_19","first-page":"100206","article-title":"Heart Sound Classification Using Signal Processing and Machine Learning Algorithms","volume":"7","author":"Zeinali","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"36955","DOI":"10.1109\/ACCESS.2021.3063129","article-title":"CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings","volume":"9","author":"Shuvo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baghel, N., Dutta, M.K., and Burget, R. (2020). Automatic Diagnosis of Multiple Cardiac Diseases from PCG Signals Using Convolutional Neural Network. Comput. Methods Programs Biomed., 197.","DOI":"10.1016\/j.cmpb.2020.105750"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1088\/0967-3334\/31\/4\/004","article-title":"Segmentation of Heart Sound Recordings by a Duration-Dependent Hidden Markov Model","volume":"31","author":"Schmidt","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_23","first-page":"822","article-title":"Logistic Regression-HSMM-Based Heart Sound Segmentation","volume":"63","author":"Springer","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Almazloum, A.A., Al-Hinnawi, A.-R., De Fazio, R., and Visconti, P. (2022). Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test\u2019s Diagnosis Using ATS and ERS Respiratory Standard Parameters. Computers, 11.","DOI":"10.3390\/computers11090130"},{"key":"ref_25","unstructured":"Bentley, P., Nordhen, G., Coimbra, M., and Mannor, S. (2023, December 19). The PASCAL Classifying Heart Sounds Challenge 2011 (CHSC2011). Available online: https:\/\/istethoscope.peterjbentley.com\/heartchallenge\/index.html#aboutdata."},{"key":"ref_26","unstructured":"(2023, December 19). HSCT11 Heart Sound Database. Available online: http:\/\/www.diit.unict.it\/hsct11\/README."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10439-006-9232-3","article-title":"Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model","volume":"35","author":"Wang","year":"2007","journal-title":"Ann. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ferreira, P.J.S., Cardoso, J.M.P., and Mendes-Moreira, J. (2020). kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning. Computers, 9.","DOI":"10.3390\/computers9040096"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Boubin, M., and Shrestha, S. (2019). Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds. Sensors, 19.","DOI":"10.3390\/s19102283"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Canziani, A., Culurciello, E., and Paszke, A. (2017, January 28\u201331). Evaluation of Neural Network Architectures for Embedded Systems. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.","DOI":"10.1109\/ISCAS.2017.8050276"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1080\/03091902.2019.1688408","article-title":"Towards Classifying Non-Segmented Heart Sound Records Using Instantaneous Frequency Based Features","volume":"43","author":"Alqudah","year":"2019","journal-title":"J. Med. Eng. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Diaz Bobillo, I.J. (2016, January 11\u201314). A Tensor Approach to Heart Sound Classification. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.184-315"},{"key":"ref_36","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3853\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:58:46Z","timestamp":1760108326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,14]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123853"],"URL":"https:\/\/doi.org\/10.3390\/s24123853","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,14]]}}}