{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:51:51Z","timestamp":1792464711519,"version":"4.5.0"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yarmouk University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Na\u00efve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.<\/jats:p>","DOI":"10.3390\/bioengineering10020249","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-3048","authenticated-orcid":false,"given":"Amjed S.","family":"Al Fahoum","sequence":"first","affiliation":[{"name":"Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ansam Omar","family":"Abu Al-Haija","sequence":"additional","affiliation":[{"name":"Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan"},{"name":"Industrial Engineering Department, JUST, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hussam A.","family":"Alshraideh","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, JUST, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","unstructured":"Labarthe, D. (2010). Epidemiology and Prevention of Cardiovascular Diseases, Jones and Bartlett Publishers. [2nd ed.]."},{"key":"ref_2","unstructured":"Jordanian Ministry of Health (2020, September 18). Periodic-Newsletters, Amman, Available online: http:\/\/www.moh.gov.jo\/EN\/Pages\/Periodic-Newsletters.aspx."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2014.08.014","article-title":"Heart monitoring systems: A review","volume":"54","author":"Jain","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"R\u00e1cz, A., Bajusz, D., and H\u00e9berger, K. (2019). Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics. Molecules, 24.","DOI":"10.3390\/molecules24152811"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Al-Fahoum, A., and Khadra, L. (2005, January 17\u201318). Combined Bispectral and Bicoherency approach for Catastrophic Arrhythmia Classification. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1616412"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"401","DOI":"10.3109\/03091902.2013.819946","article-title":"A practical reconstructed phase space approach for ECG arrhythmias classification","volume":"37","author":"Qasaimeh","year":"2013","journal-title":"J. Med. Eng. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3109\/03091902.2014.925983","article-title":"Detection of cardiac ischaemia using bispectral analysis approach","volume":"38","year":"2014","journal-title":"J. Med. Eng. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.trac.2009.09.009","article-title":"Sum of ranking differences compares methods or models fairly","volume":"29","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_9","first-page":"517","article-title":"Infarction and antiphospholipid syndrome: A first study on finger PPG waveforms effects","volume":"12","author":"Angius","year":"2012","journal-title":"Comput. Cardiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10877-007-9097-5","article-title":"The relationship between the photople-thysmographic waveform and systemic vascular resistance","volume":"21","author":"Awad","year":"2007","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"547","DOI":"10.2298\/CSIS120531015P","article-title":"Modeling of arterial stiffness using variations of pulse transit time","volume":"10","author":"Peulic","year":"2013","journal-title":"Comput. Sci. Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJBET.2015.066962","article-title":"A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects","volume":"17","author":"Seafan","year":"2015","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/TBME.2005.855725","article-title":"An Automatic Beat Detection Algorithm for Pressure Signals","volume":"52","author":"Aboy","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1109\/TBCAS.2015.2477437","article-title":"Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions","volume":"9","author":"Solosenko","year":"2015","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yousefi, M.R., Khezri, M., Bagheri, R., and Jafari, R. (2018, January 11\u201313). Automatic detection of premature ventricular contraction based on pho-toplethysmography using chaotic features and high order statistics. Proceedings of the IEEE International Symposium on Medical Meas-urements and Applications (MeMeA), Rome, Italy.","DOI":"10.1109\/MeMeA.2018.8438697"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Polania, L.F., Mestha, L.K., Huang, D.T., and Couderc, J.P. (2015, January 25\u201329). Method for classifying cardiac arrhythmias using photoplethys-mography. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Milano, Italy.","DOI":"10.1109\/EMBC.2015.7319899"},{"key":"ref_17","unstructured":"Al-haija, A.A., Alshraideh, H., and Al-Fahoum, A. (2017, January 9\u201310). Cardiorespiratory Disorders Detection Using Photoplethysmography. Proceedings of the 81st IASTEM International Conference, Phuket, Thailand."},{"key":"ref_18","unstructured":"Nano, M., Papini, G., and Fonseca, P. (2017, January 9\u201310). Comparing inter beat and inter pulse intervals from ECG and PPG signals. Proceedings of the Biomedica Summit, Eindhoven, The Netherlands."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Papini, G.B., Fonseca, P., Aubert, X.L., Overeem, S., Bergmans, J.W., and Vullings, R. (2017, January 11\u201315). Photoplethysmography beat detection and pulse morphology quality assessment for signal reliability estimation. Proceedings of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8036776"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"165181","DOI":"10.1109\/ACCESS.2019.2950220","article-title":"Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease","volume":"7","author":"Prabhakar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/240455.240463","article-title":"Data mining and knowledge discovery in databases","volume":"39","author":"Fayyad","year":"1996","journal-title":"Commun. ACM"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"595","DOI":"10.7314\/APJCP.2013.14.1.595","article-title":"Early Detection of Lung Cancer Risk Using Data Mining","volume":"14","author":"Ahmed","year":"2013","journal-title":"Asian Pac. J. Cancer Prev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"169035","DOI":"10.1155\/2013\/169035","article-title":"New Photoplethysmographic Signal Analysis Algorithm for Arterial Stiffness Estimation","volume":"2013","author":"Pilt","year":"2013","journal-title":"Sci. World J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"266","DOI":"10.2478\/v10048-012-0036-3","article-title":"The Analysis of PPG Morphology: Investigating the Effects of Aging on Arterial Compliance","volume":"12","author":"Yousef","year":"2012","journal-title":"Meas. Sci. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"392","DOI":"10.3945\/ajcn.110.001719","article-title":"Independent effects of age-related changes in waist circumference and BMI z scores in predicting cardiovascular disease risk factors in a prospective cohort of adolescent females","volume":"93","author":"Tybor","year":"2010","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"054002","DOI":"10.1088\/1361-6579\/abf9f3","article-title":"Deep learning-based photoplethysmography classification for peripheral arterial disease detection: A proof-of-concept study","volume":"42","author":"Allen","year":"2021","journal-title":"Physiol. Meas."},{"key":"ref_27","first-page":"1","article-title":"Survey: Smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis","volume":"20","author":"Shabaan","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TBME.2022.3187945","article-title":"A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography","volume":"70","author":"Olsen","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schrumpf, F., Serdack, P.R., and Fuchs, M. (2022, January 18\u201324). Regression or Classification?. Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LO, USA.","DOI":"10.1109\/CVPRW56347.2022.00236"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fakhry, M., and Brery, A.F. (2022, January 3\u20134). A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals. Proceedings of the 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco.","DOI":"10.1109\/IRASET52964.2022.9738309"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bhadri, K., Karnik, N., and Dhatrak, P. (2022, January 29\u201330). Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges. Proceedings of the 2022 10th International Conference on Emerging Trends in Engineering and Technology-Signal and Information Processing (ICETET-SIP-22), Nagpur, India.","DOI":"10.1109\/ICETET-SIP-2254415.2022.9791776"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s13246-021-01072-5","article-title":"Arrhythmia detection and classification using ECG and PPG techniques: A review","volume":"44","author":"Neha","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13661","DOI":"10.1007\/s11042-021-10805-3","article-title":"A two-stage classification model integrating feature fusion for coronary artery disease detection and classification","volume":"81","author":"Khan","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","unstructured":"(2023, January 08). Beurer PO 80 Pulse Oximeter. Available online: https:\/\/www.beurer.com\/web\/gb\/products\/medical\/pulse-oximeter\/po-80.php."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Freedman, D., Ogden, C., and Kit, B. (2015). Interrelationships between BMI, skinfold thicknesses, percent body fat and cardiovascular disease risk factors among U.S. children and adolescents. BMC Pediatr., 15.","DOI":"10.1186\/s12887-015-0493-6"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s10877-006-9050-z","article-title":"Age and Gender Do Not Influence the Ability to Detect Respiration by Photoplethysmography","volume":"20","author":"Nilsson","year":"2006","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s00392-006-0351-5","article-title":"Gender differences in the metabolic syndrome and their role for cardi-ovascular disease","volume":"95","author":"Lehmkuhl","year":"2006","journal-title":"Clin. Res. Cardiol."},{"key":"ref_38","first-page":"887","article-title":"Pulse Transit Time and Pulse Width as Potential Measure for Estimating Beat-to-Beat Systolic and Diastolic Blood Pressure","volume":"40","author":"Arza","year":"2013","journal-title":"Comput. Cardiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Samria, R., Jain, R., Jha, A., Saini, S., and Chowdhury, S. (2014, January 14\u201316). Noninvasive cuffless estimation of blood pressure using photople-thysmography without electrocardiograph Measurement. Proceedings of the International Conference IEEE Region 10 Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/TENCONSpring.2014.6863037"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1001\/jama.2013.1644","article-title":"Association of Smoking Cessation and Weight Change with Cardiovascular Disease Among Adults with and Without Diabetes","volume":"309","author":"Clair","year":"2013","journal-title":"JAMA"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e173","DOI":"10.1111\/j.1467-789X.2010.00826.x","article-title":"A systematic review of built environment factors related to physical activity and obesity risk: Implications for smart growth urban planning","volume":"12","author":"Durand","year":"2011","journal-title":"Obes. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1001\/archinternmed.2011.2","article-title":"Impact of Diabetes on Cardiovascular Disease Risk and All-Cause Mortality in Older Men","volume":"171","author":"Wannamethee","year":"2011","journal-title":"Arch. Intern. Med."},{"key":"ref_43","first-page":"18","article-title":"An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals","volume":"97","author":"Kuntamalla","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"14","DOI":"10.2174\/157340312801215782","article-title":"On the Analysis of Fingertip Photoplethysmogram Signals","volume":"8","author":"Elgendi","year":"2012","journal-title":"Curr. Cardiol. Rev."},{"key":"ref_45","first-page":"532","article-title":"Repeated measurement analysis of the area under the curve of photoplethysmogram among diabetic patients","volume":"10","author":"Usman","year":"2011","journal-title":"Life Sci. J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, J. (2003). Data Mining, Idea Group Pub.","DOI":"10.4018\/978-1-59140-051-6"},{"key":"ref_47","first-page":"27","article-title":"An Early Prediction of Cardiac Risk using Augmentation Index Developed based on a Comparative Study","volume":"49","author":"Manimegalai","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"82","DOI":"10.2478\/v10048-008-0020-0","article-title":"Non-invasive Studies on Age Related Parameters Using a Blood Volume Pulse Sensor","volume":"8","author":"Jayasree","year":"2008","journal-title":"Meas. Sci. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Beak, H., Kim, J., Kim, Y., Lee, H., and Park, K. (2007, January 8\u201311). Second derivative of photoplethysmography for estimating vascular aging. Proceedings of the 6th International Special Topic Conference on Information Technology Applications in Biomedicine, Tokyo, Japan.","DOI":"10.1109\/ITAB.2007.4407346"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1037\/a0029146","article-title":"Bayesian estimation supersedes the t test","volume":"142","author":"Kruschke","year":"2013","journal-title":"J. Exp. Psychol. Gen."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1080\/1062936X.2015.1084647","article-title":"Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters","volume":"26","author":"Bajusz","year":"2015","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Roy, K. (2017). Advances in QSAR Modeling, Springer.","DOI":"10.1007\/978-3-319-56850-8"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1080\/1062936X.2018.1505778","article-title":"Modelling methods and cross-validation variants in QSAR: A multi-level analysis","volume":"29","author":"Bajusz","year":"2018","journal-title":"SAR QSAR Environ. 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