Electrocardiogram (ECG) signal classification plays a critical role in diagnosing various cardiac conditions by identifying irregularities in heart rhythms. Despite advancements in the field, existing methodologies often rely on basic techniques that inadequately filter noise, leading to degraded performance and misinterpretation of vital features. This study presents the Spectral-Optimized Cardiac Framework (SOCF) approach to enhance the accuracy of ECG classification through advanced noise filtering, comprehensive feature extraction, efficient feature selection and integration of hybrid modelling techniques. The proposed methodology introduces the ChebWave Mean Refinement Filter (CWMRF) for effective noise reduction and to enhance signal clarity while preserving essential characteristics. In feature extraction, the Spectral Essence Extractor (SEE) captures both basic and high order features, providing deeper insights into ECG signals. Additionally, the Deep Blue Particle Optimizer (DBPO) efficiently identify relevant features while mitigating the risk of overfitting. Furthermore, the hybrid architecture of Convolutional neural network (CNN) and long short-term memory (LSTM) enable the model to effectively capture both spatial and temporal dependencies, thereby improving classification accuracy. To optimize performance, the Aquila Optimizer enhances the convergence speed and model efficiency by employing diverse search strategies inspired by the hunting behavior of Aquila bird. By integrating these advanced techniques, the SOCF achieved impressive results on the MIT-BIH dataset and PTB dataset with an accuracy of 99.6% and 99.68%, precision of 99.4% and 99.44%, recall of 99.5% and 99.51%, and F1 score of 99.2% and 99.49%, which significantly improves the robustness and reliability of ECG signal classification, ultimately providing more accurate clinical insights and better patient outcomes.
XieLLiZZhou, et al. Computational diagnostic techniques for electrocardiogram signal analysis. Sensors2020; 20(21): 6318.
2.
TalobaAIAlanaziRShahinOR, et al. Machine algorithm for heartbeat monitoring and arrhythmia detection based on ECG systems. Comput Intell Neurosci2021; 2021(1): 7677568.
3.
AmeenAFattohIEAbd El-HafeezT, et al. Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods. J Big Data2024;11(1): 159.
4.
Moreno-SánchezPAGarcía-IslaGCorinoVD, et al. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: a systematic review. Comp Biol Med2024; 172: 108235.
5.
TaiTVTanMPNTienDH, et al. A novel ECG signal Quality index method based on Skewness-MODWT analysis. 12: 70184–70197. IEEE Access2024.
6.
DhankaSMainiS.Random forest for heart disease detection: a classification approach. In 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES). IEEE. 2021, pp. 1–3
7.
BoulifAAnanouBOuladsineM, et al. A literature review: ECG-based models for arrhythmia diagnosis using artificial intelligence techniques. Bioinform Biol Insights2023; 17: 11779322221149600.
8.
IngabireHNWuKAmosJT, et al. Analysis of ECG signals by dynamic mode decomposition. IEEE J Biomed Health Inform2021; 26(5): 2124–2135.
9.
NevesIFolgadoDSantosS, et al. Interpretable heartbeat classification using local model-agnostic explanations on ECGs. Comp Biol Med2021; 133: 104393.
10.
AnbalaganTNathMKVijayalakshmiD, et al. Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Eng Adv2023; 6: 100089.
11.
DhankaSMainiS.A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets. Int J Cardiol2025; 420: 132757.
12.
UllahHBin HeyatMBAlSalmanH, et al. [Retracted] An effective and lightweight deep electrocardiography arrhythmia recognition model using novel special and native structural regularization techniques on cardiac signal. J Healthc Eng2022; 2022(1): 3408501.
13.
MostafaGMahmoudHAbd El-HafeezT, et al. The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review. BMC Med Inform Decis Making2024; 24(1): 287.
14.
AbdullaLAAl-AniMS.A review study for electrocardiogram signal classification. UHD J Sci Technol2020; 4(1): 103–117.
15.
MostafaGMahmoudHAbd El-HafeezT, et al. Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms. J Big Data2024; 11(1): 88.
16.
ZabihiFSafaraFAhadzadehB.An electrocardiogram signal classification using a hybrid machine learning and deep learning approach. Healthc Anal2024; 100366.
17.
MaLZhangF.A novel real-time detection and classification method for ECG signal images based on deep learning. Sensors2024; 24(16): 5087.
18.
AhmedAAAliWAbdullahTA, et al. Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model. Mathematics2023; 11(3): 562.
19.
ChenCHuaZZhangR, et al. Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomed Sig Process Control2020; 57: 101819.
20.
EleyanAAlboghbaishE.Electrocardiogram signals classification using deep-learning-based incorporated convolutional neural network and long short-term memory framework. Computers2024; 13(2): 55.
21.
ŚmigielSPałczyńskiKLedzińskiD.ECG signal classification using deep learning techniques based on the PTB-XL dataset. Entropy2021; 23(9): 1121.
22.
SubasiADoganSTuncerT.A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning. J Ambient Intell Humaniz Comput2023; 14(2): 711–725.
23.
IssaMFYousryATubolyG, et al. Heartbeat classification based on single lead-II ECG using deep learning. Heliyon2023; 9(7): 152: e17974.
24.
ChengJZouQZhaoY.ECG signal classification based on deep CNN and BiLSTM. BMC Med Inform Decis Making2021; 21: 1–12.
25.
DhankaSMainiS.HyOPTXGBoost and HyOPTRF: hybridized intelligent systems using optuna optimization framework for heart disease prediction with clinical interpretations. Multimedia Tools Appl2024; 83(29): 72889–72937.
26.
SharmaADhankaSKumarA, et al. A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets. Eng Res Express2024; 6(3): 035209.
27.
MaghfirohAMSetiawanSYWakidiLF.Design of single lead electrocardiography using filter order 3 to reduce noise using Spektrum analysis based on Fast Fourier Transform. Int J Adv Health Sci and Technol2023; 3(3): 133–139.
28.
Máximo-GutiérrezCHinojosaJAlvarez-MelconA.A technique for the passband and stopband control of wideband band-pass substrate integrated waveguide (SIW) filters designed with elliptic stepped impedances. AEU-Int J Electron Commun2024; 177: 155172.
29.
ChoudharyMNarwariaRP.Suppression of noise in ECG signal using low pass IIR filters. Int J Electron Comp Sci Eng2012; 1(4): 2238–2243.
30.
SongCPanLJiaoY, et al. A high-performance transmitarray antenna with thin metasurface for 5G communication based on PSO (particle swarm optimization). Sensors2020; 20(16): 4460.
31.
GuoSSWangJSGuoMW.Z-shaped transfer functions for binary particle swarm optimization algorithm. Comput Intell Neurosci2020; 2020(1): 6502807.
32.
MousaviSAfghahFAcharyaUR.HAN-ECG: an interpretable atrial fibrillation detection model using hierarchical attention networks. Comp Biol Med2020; 127: 104057.
33.
LiGTanZXuW, et al. A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification. BMC Med Inform Decision Making2021; 21: 1–15.
34.
Canqui-FloresBMelgarejo-BolivarRPTumi-FigueroaA, et al. Echocardiographic cardiac views classification using whale optimization and weighted support vector machine. Vessel Plus2024; 8.
XuWHuangKK.A scalable deep-learning based approach for R peak detection in ECG signal. J Electrocardiol2023; 78: 26–27.
37.
à MougoufanJBBFoudaJAETchuenteM, et al. Adaptive ECG beat classification by ordinal pattern based entropies. Commun Nonlinear Sci Numeric Simul2020; 84: 105156.
38.
SioudaRNemissiMSeridiH.ECG beat classification using neural classifier based on deep autoencoder and decomposition techniques. Progress Artif Intell2021; 10: 333–347.
39.
RexyJVelmaniPRajakumarT.Heart beat classification in MIT-BIH arrhythmia ECG dataset using double layer BI-LSTM model. Int J Mech Eng2021; 6: 337–344.
40.
WangJQiaoXLiuC, et al. Automated ECG classification using a non-local convolutional block attention module. Comp Meth Programs Biomed2021; 203: 106006.
41.
AtalDKSinghM.Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comp Methods Programs Biomed2020; 196: 105607.
42.
SharmaPDinkarSKGuptaDV.A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals. Neural Comput Appl2021; 33(19): 13123–13143.
43.
FaragMM.A tiny matched filter-based CNN for inter-patient ECG classification and arrhythmia detection at the edge. Sensors2023; 23(3): 1365.
44.
KumarSMallikAKumarA, et al. Fuzz-ClustNet: coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals. Comput Biol Med2023; 153: 106511.
45.
FradiMKhrijiLMachhoutM, et al. Automatic heart disease class detection using convolutional neural network architecture-based various optimizers-networks. IET Smart Cities2021; 3(1): 3–15.
46.
WangHShiHLinK, et al. A high-precision arrhythmia classification method based on dual fully connected neural network. Biomed Sig Process Control2020; 58: 101874.
47.
RafiTHKoYW.HeartNet: Self multihead attention mechanism via convolutional network with adversarial data synthesis for ECG-based arrhythmia classification. IEEE Access2022; 10: 100501–100512.
48.
PhamBTLePTTaiTC, et al. Electrocardiogram heartbeat classification for arrhythmias and myocardial infarction. Sensors2023; 23(6): 2993.
49.
BayaniAKargarM.LDCNN: a new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network. Physiol Rep2024; 12(17): e16182.
50.
CuiHNingSWangS, et al. (2025). ECG signal classification using interpretable KAN: towards predictive diagnosis of arrhythmias. Algorithms, 2025; 18(2): 90.
51.
AirlanggaG.Enhancing cardiac anomaly detection through deep learning autoencoder: an in-depth analysis using the PTB diagnostic ECG database. G-Tech: Jurnal Teknologi Terapan2024; 8(1): 584–592.
