{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:04:13Z","timestamp":1783613053763,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients\u2019 heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.<\/jats:p>","DOI":"10.3390\/s22072467","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T22:08:06Z","timestamp":1648073286000},"page":"2467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2768-4409","authenticated-orcid":false,"given":"Hongzu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Science, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4219-1699","authenticated-orcid":false,"given":"Pierre","family":"Boulanger","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: Update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","unstructured":"World Health Organization (2022, January 10). Cardiovascular Diseases. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases\/#tab=tab_3."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, H., and Boulanger, P. (2021). An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals. Sensors, 21.","DOI":"10.3390\/s21248169"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, H., and Boulanger, P. (2020). A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG). Sensors, 20.","DOI":"10.3390\/s20051461"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ge, D., Srinivasan, N., and Krishnan, S.M. (2002). Cardiac arrhythmia classification using autoregressive modeling. Biomed. Eng. Online, 1.","DOI":"10.1186\/1475-925X-1-5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.eswa.2010.07.118","article-title":"Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier","volume":"38","author":"Ceylan","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.measurement.2017.05.022","article-title":"Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities","volume":"108","author":"Sahoo","year":"2017","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.dsp.2008.09.002","article-title":"Combining recurrent neural networks with eigenvector methods for classification of ECG beats","volume":"19","year":"2009","journal-title":"Digit. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.aeue.2014.12.013","article-title":"Automatic ECG arrhythmia classification using dual tree complex wavelet based features","volume":"69","author":"Thomas","year":"2015","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.compbiomed.2017.06.006","article-title":"Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine","volume":"87","author":"Rajesh","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compbiomed.2013.11.019","article-title":"Heartbeat classification using disease-specific feature selection","volume":"46","author":"Zhang","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zihlmann, M., Perekrestenko, D., and Tschannen, M. (2017, January 24\u201327). Convolutional recurrent neural networks for electrocardiogram classification. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.070-060"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"\u015een, S.Y., and \u00d6zkurt, N. (November, January 31). ECG arrhythmia classification by using convolutional neural network and spectrogram. Proceedings of the 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), Izmir, Turkey.","DOI":"10.1109\/ASYU48272.2019.8946417"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chuah, M.C., and Fu, F. (2007). ECG anomaly detection via time series analysis. International Symposium on Parallel and Distributed Processing and Applications, Springer.","DOI":"10.1007\/978-3-540-74767-3_14"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Veeravalli, B., Deepu, C.J., and Ngo, D. (2017). Real-time, personalized anomaly detection in streaming data for wearable healthcare devices. Handbook of Large-Scale Distributed Computing in Smart Healthcare, Springer.","DOI":"10.1007\/978-3-319-58280-1_15"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.medengphy.2005.12.010","article-title":"Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification","volume":"28","author":"Christov","year":"2006","journal-title":"Med. Eng. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TBME.2006.883802","article-title":"A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features","volume":"53","author":"Reilly","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_21","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-time patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chauhan, S., and Vig, L. (2015, January 19\u201321). Anomaly detection in ECG time signals via deep long short-term memory networks. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Paris, France.","DOI":"10.1109\/DSAA.2015.7344872"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/oxfordjournals.eurheartj.a060332","article-title":"The European ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography","volume":"13","author":"Taddei","year":"1992","journal-title":"Eur. Heart J."},{"key":"ref_28","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_29","unstructured":"(MATLAB, 2021). MATLAB, Version 9.11.0 (R2021b)."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.dsp.2007.12.004","article-title":"Time\u2013frequency feature representation using energy concentration: An overview of recent advances","volume":"19","author":"Jiang","year":"2009","journal-title":"Digit. Signal Process."},{"key":"ref_31","unstructured":"Mitra, S.K., and Kuo, Y. (2006). Digital Signal Processing: A Computer-Based Approach, McGraw-Hill."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.bspc.2011.03.004","article-title":"A novel method for detecting R-peaks in electrocardiogram (ECG) signal","volume":"7","author":"Manikandan","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"52","DOI":"10.5395\/rde.2013.38.1.52","article-title":"Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis","volume":"38","author":"Kim","year":"2013","journal-title":"Restor. Dent. Endod."},{"key":"ref_35","unstructured":"You, K., Long, M., Wang, J., and Jordan, M.I. (2019). How does learning rate decay help modern neural networks?. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Prechelt, L. (1998). Early stopping-but when?. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/3-540-49430-8_3"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"16529","DOI":"10.1109\/ACCESS.2018.2807700","article-title":"An automatic cardiac arrhythmia classification system with wearable electrocardiogram","volume":"6","author":"Xia","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TBME.2010.2068048","article-title":"Heartbeat classification using feature selection driven by database generalization criteria","volume":"58","author":"Llamedo","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6498","DOI":"10.1109\/TSP.2019.2954499","article-title":"Adaptive detection of coherent radar targets in the presence of noise jamming","volume":"67","author":"Addabbo","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3276","DOI":"10.1109\/TAES.2020.2967244","article-title":"Persymmetric subspace detectors with multiple observations in homogeneous environments","volume":"56","author":"Liu","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4326","DOI":"10.1109\/TSP.2021.3095725","article-title":"Target detection within nonhomogeneous clutter via total Bregman divergence-based matrix information geometry detectors","volume":"69","author":"Hua","year":"2021","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:41:33Z","timestamp":1760136093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,23]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072467"],"URL":"https:\/\/doi.org\/10.3390\/s22072467","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,23]]}}}