{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T02:00:55Z","timestamp":1784772055950,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009096","name":"Firat University, Scientific Research Project Committee","doi-asserted-by":"publisher","award":["TEKF.26.50"],"award-info":[{"award-number":["TEKF.26.50"]}],"id":[{"id":"10.13039\/100009096","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To develop a fast and interpretable multimodal framework for the automatic detection of cardiac abnormalities using electrocardiogram (ECG) and phonocardiogram (PCG) signals. Methods: A multimodal classification scheme was designed by combining ECG and PCG recordings. For each modality, tailored preprocessing and temporal and spectral feature extraction were applied. The resulting information was fused through a quality-aware strategy that prioritized more reliable signal segments. The explainability results showed that the model focused on physiologically meaningful regions, providing supportive interpretability for its predictions. Experiments were conducted on the PhysioNet\/CinC 2016 heart sound dataset, including normal and pathological recordings, using 10-fold cross-validation. Results: The proposed method achieved a mean F1 score of 97.2%, an accuracy of 95.6%, a specificity of 88.6%, and a sensitivity of 97.7%. In addition, the lightweight preprocessing and fast feature extraction pipeline allowed the full 10-fold cross-validation procedure to be completed in only 66 s. Conclusions: The proposed ECG-PCG framework provides a fast, accurate, and interpretable solution for automated cardiac abnormality detection and appears well suited for real-time cardiac screening applications.<\/jats:p>","DOI":"10.3390\/diagnostics16142296","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:01:28Z","timestamp":1784768488000},"page":"2296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explainable Quality-Aware ECG\u2013PCG Fusion for Cardiovascular Disease Detection Using Robust Feature Modeling"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5051-9910","authenticated-orcid":false,"given":"Faiq A. Mohammed","family":"Bargarai","sequence":"first","affiliation":[{"name":"Networks Department, Bardarash Technical Institute, Akre University for Applied Sciences, Kurdistan Region, Akre 42004, Iraq"},{"name":"Electrical and Computer Engineering, College of Engineering, University of Duhok, Duhok 99454, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagvan Ali","family":"Saleh","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, College of Engineering, University of Duhok, Duhok 99454, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1614-2639","authenticated-orcid":false,"given":"Abdulkadir","family":"Sengur","sequence":"additional","affiliation":[{"name":"Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/ehjdh\/ztaa017","article-title":"Artificial intelligence-assisted auscultation in detecting congenital heart disease","volume":"2","author":"Lv","year":"2021","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.bspc.2016.08.018","article-title":"Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals","volume":"31","author":"Kumar","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.amjcard.2012.11.039","article-title":"Utility of an advanced digital electronic stethoscope in the diagnosis of coronary artery disease compared with coronary computed tomographic angiography","volume":"111","author":"Makaryus","year":"2013","journal-title":"Am. J. Cardiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1007\/s13198-022-01650-0","article-title":"PCA as an effective tool for the detection of R-peaks in an ECG signal processing","volume":"13","author":"Gupta","year":"2022","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rekha, R. (2022). Medical cyber-physical systems security. Cyber-Physical Systems and Industry 4.0, Apple Academic Press.","DOI":"10.1201\/9781003129790-5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e12894","DOI":"10.1111\/anec.12894","article-title":"Spectrum of clinical applications of interlead ECG heterogeneity assessment: From myocardial ischemia detection to sudden cardiac death risk stratification","volume":"26","author":"Verrier","year":"2021","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Amal, S., Safarnejad, L., Omiye, J.A., Ghanzouri, I., Cabot, J.H., and Ross, E.G. (2022). Use of multi-modal data and machine learning to improve cardiovascular disease care. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.840262"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107846","DOI":"10.1016\/j.bspc.2025.107846","article-title":"Hybrid CNN\u2013BiLSTM ensemble for cardiac health assessment using ECG and PCG fusion","volume":"108","author":"Kalatehjari","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104032","DOI":"10.1016\/j.rineng.2025.104032","article-title":"Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN","volume":"25","author":"Chandrasekhar","year":"2025","journal-title":"Results Eng."},{"key":"ref_10","first-page":"621","article-title":"Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds","volume":"43","author":"Potes","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_11","first-page":"613","article-title":"Heart sound anomaly and quality detection using ensemble of neural networks without segmentation","volume":"43","author":"Zabihi","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"160882","DOI":"10.1109\/ACCESS.2020.3020806","article-title":"Time-frequency analysis, denoising, compression, segmentation, and classification of PCG signals","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"813","article-title":"Classifying heart sound recordings using deep convolutional neural networks and Mel-frequency cepstral coefficients","volume":"43","author":"Rubin","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_14","first-page":"565","article-title":"Heart sound classification using deep structured features","volume":"43","author":"Tschannen","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_15","first-page":"617","article-title":"Heart sound classification based on temporal alignment techniques","volume":"43","author":"Phoo","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_16","unstructured":"Homsi, M.N., Medina, N., Hernandez, M., Quintero, N., Perpi\u00f1an, G., Quintana, A., and Warrick, P. (2016). Automatic heart sound recording classification using a nested set of ensemble algorithms. Comput. Cardiol., 43."},{"key":"ref_17","first-page":"585","article-title":"Normal\/abnormal heart sound recordings classification using convolutional neural network","volume":"43","author":"Nilanon","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s13246-020-00875-2","article-title":"Recognition of cardiac abnormalities from synchronized ECG and PCG signals","volume":"43","author":"Chakir","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_19","first-page":"193","article-title":"Heart sound classification using Markov chain analysis and ensemble learning","volume":"45","author":"Vernekar","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Grzegorczyk, I., Soli\u0144ski, M., \u0141epek, M., Perka, A., Rosi\u0144ski, J., Rymko, J., St\u0119pie\u0144, K., and Giera\u0142towski, J. (2016). PCG classification using a neural network approach. Comput. Cardiol., 43.","DOI":"10.22489\/CinC.2016.323-252"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Goda, M.\u00c1., and Hajas, P. (2016). Morphological determination of pathological PCG signals by time and frequency domain analysis. Comput. Cardiol., 43.","DOI":"10.22489\/CinC.2016.324-249"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huber, P.J. (1981). Robust Statistics, Wiley.","DOI":"10.1002\/0471725250"},{"key":"ref_23","unstructured":"Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., and Stahel, W.A. (1986). Robust Statistics: The Approach Based on Influence Functions, Wiley."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","article-title":"The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms","volume":"15","author":"Welch","year":"1967","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bendat, J.S., and Piersol, A.G. (2010). Random Data: Analysis and Measurement Procedures, Wiley. [4th ed.].","DOI":"10.1002\/9781118032428"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/49.608","article-title":"Transform coding of audio signals using perceptual noise criteria","volume":"6","author":"Johnston","year":"1988","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Flanagan, J.L. (1972). Speech Analysis, Synthesis and Perception, Springer. [2nd ed.].","DOI":"10.1007\/978-3-662-01562-9"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Daubechies, I. (1992). Ten Lectures on Wavelets, SIAM.","DOI":"10.1137\/1.9781611970104"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_30","unstructured":"Oppenheim, A.V., and Schafer, R.W. (2010). Discrete-Time Signal Processing, Prentice Hall. [3rd ed.]."},{"key":"ref_31","unstructured":"Mallat, S. (2009). A Wavelet Tour of Signal Processing: The Sparse Way, Academic Press. [3rd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation by wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"R155","DOI":"10.1088\/0967-3334\/26\/5\/R01","article-title":"Wavelet transforms and the ECG: A review","volume":"26","author":"Addison","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/10.979357","article-title":"An advanced detrending method with application to HRV analysis","volume":"49","author":"Tarvainen","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zadrozny, B., and Elkan, C. (2002, January 23\u201326). Transforming classifier scores into accurate multiclass probability estimates. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada.","DOI":"10.1145\/775047.775151"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, J., Ke, L., Du, Q., Ding, X., and Chen, X. (2022). Research on the classification of ECG and PCG signals based on BiLSTM-GoogLeNet-DS. Appl. Sci., 12.","DOI":"10.3390\/app122211762"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/5.554205","article-title":"An introduction to multisensor data fusion","volume":"85","author":"Hall","year":"1997","journal-title":"Proc. IEEE"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cheng, J., Zou, Q., and Zhao, Y. (2021). ECG signal classification based on deep CNN and BiLSTM. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01736-y"},{"key":"ref_41","first-page":"381","article-title":"Heart abnormality classification using PCG and ECG recordings","volume":"25","author":"Singh","year":"2021","journal-title":"Comput. Sist."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The precision\u2013recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1097\/01.ede.0000147512.81966.ba","article-title":"Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples","volume":"16","author":"Schisterman","year":"2005","journal-title":"Epidemiology"},{"key":"ref_45","first-page":"581","article-title":"Can electrocardiogram classification be applied to phonocardiogram data? An analysis using recurrent neural networks","volume":"43","author":"Dominik","year":"2016","journal-title":"Comput. Cardiol."},{"key":"ref_46","first-page":"104272","article-title":"WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values","volume":"138","author":"Nayebi","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100700","DOI":"10.1109\/ACCESS.2022.3207765","article-title":"Explainable AI for time series classification: A review, taxonomy and research directions","volume":"10","author":"Theissler","year":"2022","journal-title":"IEEE Access"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:09:41Z","timestamp":1784768981000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16142296"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16142296","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]}}}