{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:08:21Z","timestamp":1775146101893,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Cardiac arrhythmia (ARR) and congestive heart failure (CHF) are heart diseases that can cause dysfunction of other body organs and possibly death. This paper describes a fast and accurate detection system to distinguish between ARR and normal sinus (NS), and between CHF and NS. Methods: the proposed automatic detection system uses the higher amplitude coefficients (HAC) of the discrete cosine transform (DCT) of the electrocardiogram (ECG) as discriminant features to distinguish ARR and CHF signals from NS. The approach is tested with three statistical classifiers, of which the k-nearest neighbors (k-NN) algorithm. Results: the DCT provides fast compression of the ECG signal, and statistical tests show that the obtained HACs are different from ARR and NS, and for CHF and NS. The latter achieved highest accuracy under ten-fold cross-validation in comparison to Na\u00efve Bayes (NB) and nonlinear support vector machine (SVM). The kNN yielded 97% accuracy, 99% sensitivity, 90% specificity and 0.63 s processing time when classifying ARR against NS, and it yielded 99% accuracy, 99.7% sensitivity, and 99.2% specificity, and 0.27 seconds processing time when classifying HCF against NS. In addition to a fast response, the DCT-kNN system yields higher accuracy in comparison to recent works. Conclusions: it is concluded that using the DCT based HACs as biomarkers of ARR and CHF can lead an efficient computer aided diagnosis (CAD) system which is fast, accurate and does not require ECG signal pre-processing and segmentation. The proposed system is promising for applications in clinical milieu.<\/jats:p>","DOI":"10.3390\/diagnostics15070849","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:45:51Z","timestamp":1743144351000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9237-4100","authenticated-orcid":false,"given":"Salim","family":"Lahmiri","sequence":"first","affiliation":[{"name":"Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9376-8726","authenticated-orcid":false,"given":"Stelios","family":"Bekiros","sequence":"additional","affiliation":[{"name":"Valter Cantino Department of Management, University of Turin (UniTo), 10124 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.bspc.2012.10.005","article-title":"Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning","volume":"8","author":"Javadi","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.bspc.2014.08.010","article-title":"Heart diseases prediction based on ECG signals\u2019 classification using a genetic-fuzzy system and dynamical model of ECG signals","volume":"14","author":"Vafaie","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1016\/j.eswa.2015.06.046","article-title":"A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines","volume":"42","author":"Khalaf","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.bspc.2016.07.010","article-title":"Heartbeat classification using projected and dynamic features of ECG signal","volume":"31","author":"Chen","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.bspc.2017.12.004","article-title":"Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier","volume":"41","author":"Rajesh","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, W., Zheng, L., Li, K., Wang, Q., Liu, G., and Jiang, Q. (2016). A novel and effective method for congestive heart failure detection and quantification using dynamic heart rate variability measurement. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0165304"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ijmedinf.2017.09.006","article-title":"Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics","volume":"108","author":"Mahajan","year":"2017","journal-title":"Int. J. Med. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.bspc.2016.08.014","article-title":"Multi-scale transitions of fuzzy sample entropy of RR-intervals and their phase-randomized surrogates: A possibility to diagnose congestive heart failure","volume":"31","author":"Zandiyeh","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.physa.2017.04.091","article-title":"Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure","volume":"482","author":"Bhaduri","year":"2017","journal-title":"Physica A"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.bspc.2018.01.001","article-title":"Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects","volume":"42","author":"Wang","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/T-C.1974.223784","article-title":"Discrete cosinetransform","volume":"C-23","author":"Ahmed","year":"1974","journal-title":"IEEE Trans. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","article-title":"On the optimality of the simple Bayesian classifier under zero-one loss","volume":"29","author":"Domingos","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Everitt, B.S., Landau, S., Leese, M., and Stahl, D. (2011). Miscellaneous clustering methods. Cluster Analysis, John Wiley & Sons, Ltd.. [5th ed.].","DOI":"10.1002\/9780470977811"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.mcm.2009.05.004","article-title":"Improvement of the Discrete Cosine Transform calculation by means of a recursive method","volume":"50","author":"Signes","year":"2009","journal-title":"Math. Comput. Model."},{"key":"ref_17","unstructured":"(2024, September 01). Available online: https:\/\/physionet.org\/cgi-bin\/atm\/ATM."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.physa.2018.08.125","article-title":"Chaos based nonlinear analysis to study cardiovascular responses to changes in posture","volume":"512","author":"Ghosh","year":"2018","journal-title":"Physica A"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"125021","DOI":"10.1016\/j.physa.2020.125021","article-title":"Identification of healthy and pathological heartbeat dynamics based on ECG-waveform using multifractal spectrum","volume":"559","author":"Yang","year":"2020","journal-title":"Physica A"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.physa.2016.11.102","article-title":"Multiscale permutation entropy analysis of electrocardiogram","volume":"471","author":"Liu","year":"2017","journal-title":"Physica A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1016\/j.physa.2012.01.019","article-title":"Scale invariance analysis of the premature ECG signals","volume":"391","author":"Wang","year":"2012","journal-title":"Physica A"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123234","DOI":"10.1016\/j.physa.2019.123234","article-title":"The chaotic characteristics detection based on multifractal detrended fluctuation analysis of the elderly 12-lead ECG signals","volume":"540","author":"Jiao","year":"2020","journal-title":"Physica A"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.physa.2018.09.020","article-title":"Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis","volume":"514","author":"Cui","year":"2019","journal-title":"Physica A"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"122433","DOI":"10.1016\/j.physa.2019.122433","article-title":"Signature of complexity in time-frequency domain","volume":"535","author":"Yan","year":"2019","journal-title":"Physica A"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100142","DOI":"10.1016\/j.health.2023.100142","article-title":"A nonlinear analysis of cardiovascular diseases using multi-scale analysis and generalized hurst exponent","volume":"3","author":"Lahmiri","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100171","DOI":"10.1016\/j.health.2023.100171","article-title":"A wavelet leaders model with multiscale entropy measures for diagnosing arrhythmia and congestive heart failure","volume":"3","author":"Lahmiri","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111610","DOI":"10.1016\/j.chaos.2021.111610","article-title":"Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur","volume":"154","author":"Lahmiri","year":"2022","journal-title":"Chaos Solitons Fractals"},{"key":"ref_28","first-page":"104","article-title":"Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains","volume":"1","author":"Lahmiri","year":"2014","journal-title":"Healthc. Technol. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112972","DOI":"10.1016\/j.chaos.2022.112972","article-title":"Optimal tuning of support vector machines and k-NN algorithm by using Bayesian optimization for newborn cry signal diagnosis based on audio signal processing features","volume":"167","author":"Lahmiri","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lahmiri, S. (2023). Integrating convolutional neural networks, kNN, and Bayesian optimization for efficient diagnosis of Alzheimer\u2019s disease in magnetic resonance images. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104375"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7204","DOI":"10.1002\/int.22586","article-title":"MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification","volume":"36","author":"Zheng","year":"2021","journal-title":"Int. J. Intell. Syst."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/7\/849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:02:49Z","timestamp":1760029369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/7\/849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":31,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["diagnostics15070849"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15070849","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]}}}