21.10.7 Heart Analysis, ECG, Electrocardiogram, Other Electrical Signals

Chapter Contents (Back)
Electrocardiogram. ECG. For Biometrics:
See also ECG, Electrocardiogram for Biometrics.
See also Heart, Cardiac, Echocardiography, Ultrasound.
See also Atrial Fibrillation.

Birman, K.P.,
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Koski, A.,
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ECG-correlated imaging of the heart with subsecond multislice spiral CT,
MedImg(19), No. 9, September 2000, pp. 888-901.
IEEE Top Reference. 0110
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ECG to identify individuals,
PR(38), No. 1, January 2005, pp. 133-142.
Elsevier DOI 0410
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Güler, I.[Inan], Übeyli, E.D.[Elif Derya],
ECG beat classifier designed by combined neural network model,
PR(38), No. 2, February 2005, pp. 199-208.
Elsevier DOI 0411

See also recurrent neural network classifier for Doppler ultrasound blood flow signals, A.
See also Features extracted by eigenvector methods for detecting variability of EEG signals. BibRef

Engin, M.[Mehmet],
ECG beat classification using neuro-fuzzy network,
PRL(25), No. 15, November 2004, pp. 1715-1722.
Elsevier DOI 0411
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Froese, T.[Tom], Hadjiloucas, S.[Sillas], Galvão, R.K.H.[Roberto K.H.], Becerra, V.M.[Victor M.], Coelho, C.J.[Clarimar José],
Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms,
PRL(27), No. 5, 1 April 2006, pp. 393-407.
Elsevier DOI 0604
Discrete wavelet transform; Neural networks; Genetic algorithms; Linear discriminant analysis BibRef

Zandi, A.S.[Ali Shahidi], Moradi, M.H.[Mohammad Hassan],
Quantitative evaluation of a wavelet-based method in ventricular late potential detection,
PR(39), No. 7, July 2006, pp. 1369-1379.
Elsevier DOI 0606
Ventricular late potentials (VLPs); High-resolution ECG (HRECG); Wavelet transform (WT); Artificial neural network (ANN); Principal component analysis (PCA) BibRef

Liu, Z., Liu, C., He, B.,
Noninvasive Reconstruction of Three-Dimensional Ventricular Activation Sequence From the Inverse Solution of Distributed Equivalent Current Density,
MedImg(25), No. 10, October 2006, pp. 1307-1318.
IEEE DOI 0609
BibRef

Benzid, R., Marir, F., Bouguechal, N.E.,
Electrocardiogram Compression Method Based on the Adaptive Wavelet Coefficients Quantization Combined to a Modified Two-Role Encoder,
SPLetters(14), No. 6, June 2007, pp. 373-376.
IEEE DOI 0706
BibRef

Yu, S.N.[Sung-Nien], Chen, Y.H.[Ying-Hsiang],
Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network,
PRL(28), No. 10, 15 July 2007, pp. 1142-1150.
Elsevier DOI 0706
Electrocardiogram; RR interval; Discrete wavelet transform; Probabilistic neural network; Pattern classification BibRef

Boucheham, B.[Bachir],
Matching of quasi-periodic time series patterns by exchange of block-sorting signatures,
PRL(29), No. 4, 1 March 2008, pp. 501-514.
Elsevier DOI 0711
Pattern matching; Time series; Block-sorting signature; Shape exchange; DTW ECG application BibRef

Boucheham, B.[Bachir],
Reduced data similarity-based matching for time series patterns alignment,
PRL(31), No. 7, 1 May 2010, pp. 629-638.
Elsevier DOI 1004
Time series similarity; Pattern matching; Data reduction; Datamining; Data retrieval; Person identification using ECG BibRef

Noponen, K.[Kai], Kortelainen, J.[Jukka], Seppanen, T.[Tapio],
Invariant trajectory classification of dynamical systems with a case study on ECG,
PR(42), No. 9, September 2009, pp. 1832-1844.
Elsevier DOI 0905
Eigenvalues and eigenfunctions; Electrocardiography; Group theory; Least squares methods; Multidimensional signal processing; Nonlinear systems; Pattern recognition; Shape BibRef

Martis, R.J.[Roshan Joy], Chakraborty, C.[Chandan], Ray, A.K.[Ajoy K.],
A two-stage mechanism for registration and classification of ECG using Gaussian mixture model,
PR(42), No. 11, November 2009, pp. 2979-2988.
Elsevier DOI 0907
ECG; Pan Tompkins algorithm; Linear prediction; PCA; GMM; Chernoff bound; Bhattacharya bound BibRef

Camargo-Olivares, J.L., Martin-Clemente, R., Hornillo-Mellado, S., Elena, M.M., Roman, I.,
The Maternal Abdominal ECG as Input to MICA in the Fetal ECG Extraction Problem,
SPLetters(18), No. 3, March 2011, pp. 161-164.
IEEE DOI 1102
BibRef

Janssen, J.H., Bailenson, J.N., Ijsselsteijn, W.A., Westerink, J.H.D.M.,
Intimate Heartbeats: Opportunities for Affective Communication Technology,
AffCom(1), No. 2, July-December 2010, pp. 72-80.
IEEE DOI 1202
BibRef

Lekadir, K., Hoogendoorn, C., Pereanez, M., Alba, X., Pashaei, A., Frangi, A.F.,
Statistical Personalization of Ventricular Fiber Orientation Using Shape Predictors,
MedImg(33), No. 4, April 2014, pp. 882-890.
IEEE DOI 1404
Diffusion tensor imaging BibRef

Liu, C., Eggen, M.D., Swingen, C.M., Iaizzo, P.A., He, B.,
Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms,
MedImg(31), No. 9, September 2012, pp. 1777-1785.
IEEE DOI 1209
BibRef

Kurihara, Y., Watanabe, K.,
Sleep-Stage Decision Algorithm by Using Heartbeat and Body-Movement Signals,
SMC-A(42), No. 6, November 2012, pp. 1450-1459.
IEEE DOI 1210
BibRef

Dong, J.[Jun], Zhang, J.W.[Jia-Wei], Zhu, H.H.[Hong-Hai], Wang, L.P.[Li-Ping], Liu, X.[Xia], Li, Z.J.[Zhen-Jiang],
A Remote Diagnosis Service Platform for Wearable ECG Monitors,
IEEE_Int_Sys(27), No. 6, November-December 2012, pp. 36-43.
IEEE DOI 1212
BibRef

Zhao, J., Butters, T.D., Zhang, H., Le Grice, I.J., Sands, G.B., Smaill, B.H.,
Image-Based Model of Atrial Anatomy and Electrical Activation: A Computational Platform for Investigating Atrial Arrhythmia,
MedImg(32), No. 1, January 2013, pp. 18-27.
IEEE DOI 1301
BibRef

Sharma, L.N., Dandapat, S., Mahanta, A.,
Kurtosis-based noise estimation and multiscale energy to denoise ECG signal,
SIViP(7), No. 2, March 2013, pp. 235-245.
WWW Link. 1303
BibRef

Zou, Y., Han, J., Weng, X.Z., Zeng, X.,
An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform,
SPLetters(20), No. 5, May 2013, pp. 515-518.
IEEE DOI 1304
electrocardiogram related applications BibRef

Xu, G.W.[Gao-Wei], Han, J.[Jun], Zou, Y.[Yao], Zeng, X.Y.[Xiao-Yang],
A 1.5-D Multi-Channel EEG Compression Algorithm Based on NLSPIHT,
SPLetters(22), No. 8, August 2015, pp. 1118-1122.
IEEE DOI 1502
computational complexity BibRef

Nielsen, B.F., Lysaker, M., Grottum, P.,
Computing Ischemic Regions in the Heart With the Bidomain Model: First Steps Towards Validation,
MedImg(32), No. 6, 2013, pp. 1085-1096.
IEEE DOI 1307
bioelectric potentials; electrocardiography; ischemic region identification BibRef

Frank, J., Mannor, S., Pineau, J., Precup, D.,
Time Series Analysis Using Geometric Template Matching,
PAMI(35), No. 3, March 2013, pp. 740-754.
IEEE DOI 1303
wearable sensor, ECG data. BibRef

Ramakrishnan, A.G., Prathosh, A.P., Ananthapadmanabha, T.V.,
Threshold-Independent QRS Detection Using the Dynamic Plosion Index,
SPLetters(21), No. 5, May 2014, pp. 554-558.
IEEE DOI 1404
electrocardiography BibRef

Geselowitz, D.B.,
STARS: Electrocardiography,
PIEEE(102), No. 3, March 2014, pp. 399-404.
IEEE DOI 1404
Review of our past articles. Biomedical equipment; Cardiography; Electrocardiography; History BibRef

Erem, B., van Dam, P.M., Brooks, D.H.,
Identifying Model Inaccuracies and Solution Uncertainties in Noninvasive Activation-Based Imaging of Cardiac Excitation Using Convex Relaxation,
MedImg(33), No. 4, April 2014, pp. 902-912.
IEEE DOI 1404
Heart BibRef

Erem, B., Coll-Font, J., Martinez Orellana, R., St'ovicek, P., Brooks, D.H.,
Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry,
MedImg(33), No. 3, March 2014, pp. 726-738.
IEEE DOI 1404
bioelectric potentials BibRef

Jung, S.J.[Sang-Joong], Shin, H.S.[Heung-Sub], Chung, W.Y.[Wan-Young],
Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel,
IET-ITS(8), No. 1, February 2014, pp. 43-50.
DOI Link 1406
Award, IET IT Premium. automotive components BibRef

Alajlan, N.[Naif], Bazi, Y.[Yakoub], Melgani, F.[Farid], Malek, S.[Salim], Bencherif, M.A.[Mohamed A.],
Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods,
SIViP(8), No. 5, July 2014, pp. 931-942.
Springer DOI 1407
BibRef

Baali, H., Akmeliawati, R., Salami, M.J.E., Khorshidtalab, A., Lim, E.,
ECG Parametric Modeling Based on Signal Dependent Orthogonal Transform,
SPLetters(21), No. 10, October 2014, pp. 1293-1297.
IEEE DOI 1407
Biological system modeling BibRef

Balouchestani, M.[Mohammadreza], Raahemifar, K.[Kaamran], Krishnan, S.[Sridhar],
Low sampling rate algorithm for wireless ECG systems based on compressed sensing theory,
SIViP(9), No. 3, March 2015, pp. 527-533.
Springer DOI 1503
BibRef

Balouchestani, M.[Mohammadreza], Krishnan, S.[Sridhar],
Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach,
SIViP(10), No. 1, January 2016, pp. 113-120.
WWW Link. 1601
BibRef

Dasilva, P.[Patrick], Fortier, P.[Paul], Sethares, K.[Kristen],
Electrocardiogram Classification Sensor System Supporting an Autonomous Mobile Cardiovascular Disease Detection Aid,
Sensors(184), No. 1, January 2015, pp. 92-100.
HTML Version. 1504
BibRef

Laguna, P., Cortes, J.P.M.[J.P. Martinez], Pueyo, E.,
Techniques for Ventricular Repolarization Instability Assessment From the ECG,
PIEEE(104), No. 2, February 2016, pp. 392-415.
IEEE DOI 1601
Arrhythmia BibRef

Porta, A., Faes, L.,
Wiener-Granger Causality in Network Physiology With Applications to Cardiovascular Control and Neuroscience,
PIEEE(104), No. 2, February 2016, pp. 282-309.
IEEE DOI 1601
Biomedical signal processing BibRef

Afkhami, R.G.[Rashid Ghorbani], Azarnia, G.[Ghanbar], Tinati, M.A.[Mohammad Ali],
Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals,
PRL(70), No. 1, 2016, pp. 45-51.
Elsevier DOI 1602
Decision tree BibRef

Kang, S.J., Lee, S.Y., Cho, H.I., Park, H.,
ECG Authentication System Design Based on Signal Analysis in Mobile and Wearable Devices,
SPLetters(23), No. 6, June 2016, pp. 805-808.
IEEE DOI 1606
authorisation BibRef

Alessandrini, M., Heyde, B., Queirós, S., Cygan, S., Zontak, M., Somphone, O., Bernard, O., Sermesant, M., Delingette, H., Barbosa, D., de Craene, M., O'Donnell, M., d'Hooge, J.,
Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings,
MedImg(35), No. 8, August 2016, pp. 1915-1926.
IEEE DOI 1608
Optical imaging BibRef

Gravina, R., Fortino, G.,
Automatic Methods for the Detection of Accelerative Cardiac Defense Response,
AffCom(7), No. 3, July 2016, pp. 286-298.
IEEE DOI 1609
Acceleration BibRef

Rajankar, S.[Supriya], Talbar, S.[Sanjay],
A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree,
SIViP(10), No. 8, November 2016, pp. 1559-1566.
WWW Link. 1610
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Charvátová, H.[Hana], Procházka, A.[Aleš], Vaseghi, S.[Saeed], Vyšata, O.[Oldrich], Vališ, M.[Martin],
GPS-based analysis of physical activities using positioning and heart rate cycling data,
SIViP(11), No. 2, February 2017, pp. 251-258.
Springer DOI 1702
BibRef

Milchevski, A.[Aleksandar], Gusev, M.[Marjan],
Improved pipelined wavelet implementation for filtering ECG signals,
PRL(95), No. 1, 2017, pp. 85-90.
Elsevier DOI 1708
DSP BibRef

Rodrigo, M., Climent, A.M., Liberos, A., Hernandez-Romero, I., Arenal, Á., Bermejo, J., Fernandez-Aviles, F., Atienza, F., Guillem, M.S.,
Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality,
MedImg(37), No. 3, March 2018, pp. 733-740.
IEEE DOI 1804
diseases, electrocardiography, image reconstruction, inverse problems, medical image processing, AF patients, inverse methods BibRef

Bassiouni, M.M.[Mahmoud M.], El-Dahshan, E.S.A.[El-Sayed A.], Khalefa, W.[Wael], Salem, A.M.[Abdelbadeeh M.],
Intelligent hybrid approaches for human ECG signals identification,
SIViP(12), No. 5, July 2018, pp. 941-949.
Springer DOI 1806
BibRef

Fu, D.P.[Da-Peng], Xia, Z.R.[Zhou-Rui], Gao, P.F.[Peng-Fei], Wang, H.Q.[Hai-Qing], Lin, J.P.[Jian-Ping], Sun, L.[Li],
ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier,
IEICE(E101-D), No. 8, August 2018, pp. 2082-2091.
WWW Link. 1808
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Carrera, D.[Diego], Rossi, B.[Beatrice], Fragneto, P.[Pasqualina], Boracchi, G.[Giacomo],
Online anomaly detection for long-term ECG monitoring using wearable devices,
PR(88), 2019, pp. 482-492.
Elsevier DOI 1901
Online and long-term ECG monitoring, Anomaly detection, Domain adaptation, Wearable devices, Sparse representations BibRef

Xiong, F.[Fan], Chen, D.Y.[Dong-Yi], Chen, Z.H.[Zheng-Hao], Dai, S.[Shumei],
Cancellation of motion artifacts in ambulatory ECG signals using TD-LMS adaptive filtering techniques,
JVCIR(58), 2019, pp. 606-618.
Elsevier DOI 1901
Adaptive cancellation algorithm, Motion artifacts, Auxiliary dry electrode, Cosine transform BibRef

Li, C., Lin, D., Lü, J., Hao, F.,
Cryptanalyzing an Image Encryption Algorithm Based on Autoblocking and Electrocardiography,
MultMedMag(25), No. 4, October 2018, pp. 46-56.
IEEE DOI 1901
Encryption, Electrocardiography, Chaotic communication, Feature extraction, Logistics, * BibRef

Yang, T., Pogwizd, S.M., Walcott, G.P., Yu, L., He, B.,
Noninvasive Activation Imaging of Ventricular Arrhythmias by Spatial Gradient Sparse in Frequency Domain: Application to Mapping Reentrant Ventricular Tachycardia,
MedImg(38), No. 2, February 2019, pp. 525-539.
IEEE DOI 1902
Imaging, Electric potential, Myocardium, Frequency-domain analysis, Electrodes, Inverse problems, Image reconstruction, ventricular arrhythmia BibRef

Yousofvand, L.[Leila], Fathi, A.[Abdolhossein], Abdali-Mohammadi, F.[Fardin],
Person identification using ECG signal's symbolic representation and dynamic time warping adaptation,
SIViP(13), No. 2, March 2019, pp. 245-251.
Springer DOI 1904
BibRef

Goshvarpour, A.[Ateke], Goshvarpour, A.[Atefeh],
Gender and age classification using a new Poincare section-based feature set of ECG,
SIViP(13), No. 3, April 2019, pp. 531-539.
WWW Link. 1904
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Alawad, M., Wang, L.,
Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms,
MedImg(38), No. 5, May 2019, pp. 1172-1184.
IEEE DOI 1905
Data models, Electrocardiography, Adaptation models, Solid modeling, Torso, Real-time systems, domain adaptation BibRef

Dinakarrao, S.M.P.[Sai Manoj Pudukotai], Jantsch, A.[Axel], Shafique, M.[Muhammad],
Computer-aided Arrhythmia Diagnosis with Bio-signal Processing: A Survey of Trends and Techniques,
Surveys(51), No. 1, February 2019, pp. Article No 23.
DOI Link 1906
Signals obtained from a patient, i.e., bio-signals, are utilized to analyze the health of patient. One such bio-signal of paramount importance is the electrocardiogram (ECG) BibRef

Feli, M.[Mohammad], Abdali-Mohammadi, F.[Fardin],
A novel recursive backtracking genetic programming-based algorithm for 12-lead ECG compression,
SIViP(13), No. 5, July 2019, pp. 1029-1036.
WWW Link. 1906
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Srivastva, R.[Ranjeet], Singh, Y.N.[Yogendra Narain],
ECG analysis for human recognition using non-fiducial methods,
IET-Bio(8), No. 5, September 2019, pp. 295-305.
DOI Link 1908
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Zhang, Y.[Yue], Xiao, Z.B.[Zhi-Bo], Guo, Z.H.[Zhen-Hua], Wang, Z.L.[Zi-Liang],
ECG-based personal recognition using a convolutional neural network,
PRL(125), 2019, pp. 668-676.
Elsevier DOI 1909
Deep convolutional neural network, ECG, Feature representation, Personal recognition, Voting BibRef

Sharma, M.[Manish], Acharya, U.R.[U. Rajendra],
A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank,
PRL(125), 2019, pp. 235-240.
Elsevier DOI 1909
Coronary artery disease, ECG, Time-frequency concentration, Support Vector Machine (SVM) BibRef

Ghimire, S., Sapp, J.L., Horácek, B.M., Wang, L.,
Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Error,
MedImg(38), No. 11, November 2019, pp. 2582-2595.
IEEE DOI 1911
Electrocardiography, Predictive models, Mathematical model, Data models, Heart, Inverse problems, Image reconstruction, graphical model BibRef

Tseng, C.H.[Chi-Ho], Lin, C.[Chen], Chang, H.C.[Hsiang-Chih], Liu, C.C.[Cyuan-Cin], Serafico, B.M.F.[Bess Ma F.], Wu, L.C.[Li-Ching], Lin, C.T.[Chih-Ting], Hsu, T.[Tien], Huang, C.Y.[Chun-Yao], Lo, M.T.[Men-Tzung],
Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening,
Computer(52), No. 11, November 2019, pp. 40-51.
IEEE DOI 1911
Electrocardiography, Monitoring, Cloud computing, Servers, Biomedical monitoring, Databases, Artificial intelligence BibRef

Kim, J.[Jeehoon], Sung, D.[Dongsuk], Koh, M.[MyungJun], Kim, J.[Jason], Park, K.S.[Kwang Suk],
Electrocardiogram authentication method robust to dynamic morphological conditions,
IET-Bio(8), No. 6, November 2019, pp. 401-410.
DOI Link 1911
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Abdalla, F.Y.O.[Fakheraldin Y. O.], Wu, L.[Longwen], Ullah, H.[Hikmat], Ren, G.H.[Guang-Hui], Noor, A.[Alam], Zhao, Y.Q.[Ya-Qin],
ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition,
SIViP(13), No. 7, October 2019, pp. 1283-1291.
Springer DOI 1911
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Li, R.[Rui], Yang, G.[Gongping], Wang, K.[Kuikui], Huang, Y.[Yuwen], Yuan, F.[Feng], Yin, Y.L.[Yi-Long],
Robust ECG biometrics using GNMF and sparse representation,
PRL(129), 2020, pp. 70-76.
Elsevier DOI 2001
ECG, Biometrics, GNMF, Sparse representation, 1 norm BibRef

Mo, X.Z.[Xiao-Zhu], Ling, B.W.K.[Bingo Wing-Kuen], Ye, Q.L.[Qiu-Liang], Zhou, Y.[Yang],
Linear phase properties of the singular spectrum analysis components for the estimations of the RR intervals of electrocardiograms,
SIViP(14), No. 2, March 2020, pp. 325-332.
Springer DOI 2003
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Ullah, A.[Amin], Anwar, S.M.[Syed Muhammad], Bilal, M.[Muhammad], Mehmood, R.M.[Raja Majid],
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
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Hsieh, J., Hung, K., Liu, J., Wu, T.,
Wavelet-Based Quality-Constrained ECG Data Compression System Without Decoding Process,
MultMedMag(27), No. 2, April 2020, pp. 33-45.
IEEE DOI 2006
Electrocardiography, Encoding, Quantization (signal), Data compression, Discrete wavelet transforms, genetic algorithm BibRef

Jagannath, D.J., Dolly, D.R.J.[D. Raveena Judie], Peter, J.D.[J. Dinesh],
Deep learning strategies for foetal electrocardiogram signal synthesis,
PRL(136), 2020, pp. 286-292.
Elsevier DOI 2008
Deep learning, Foetal electrocardiogram, Convolutional neural network, Deep belief neural network, Back propagation neural network BibRef

Pandey, S.K.[Saroj Kumar], Janghel, R.R.[Rekh Ram],
Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model,
SIViP(14), No. 6, September 2020, pp. 1255-1263.
WWW Link. 2008
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Kheirati Roonizi, A.,
A New Approach to Gaussian Signal Smoothing: Application to ECG Components Separation,
SPLetters(27), 2020, pp. 1924-1928.
IEEE DOI 2011
Electrocardiography, Kalman filters, Noise measurement, State estimation, Mathematical model, Atmospheric modeling, state estimation BibRef

Tripathi, K.[Kirthi], Sohal, H.[Harsh], Jain, S.[Shruti],
Statistical Analysis of HRV Parameters for the Detection of Arrhythmia,
IJIG(20), No. 4, October 2020, pp. 2050036.
DOI Link 2011
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Mathivanan, P., Ganesh, A.B.[A. Balaji],
ECG steganography based on tunable Q-factor wavelet transform and singular value decomposition,
IJIST(31), No. 1, 2021, pp. 270-287.
DOI Link 2102
ECG steganography, scaling factor and performance metrics, singular value decomposition (SVD), tunable Q-factor wavelet transform (TQWT) BibRef

El Ogri, O.[Omar], Karmouni, H.[Hicham], Sayyouri, M.[Mhamed], Qjidaa, H.[Hassan],
3D image recognition using new set of fractional-order Legendre moments and deep neural networks,
SP:IC(98), 2021, pp. 116410.
Elsevier DOI 2109
Fractional-order orthogonal polynomials, Fractional-order moment invariants, 3D image analysis, Deep neural networks BibRef

Daoui, A., Yamni, M., Karmouni, H.[Hicham], El Ogri, O.[Omar], Sayyouri, M.[Mhamed], Qjidaa, H.[Hassan],
Efficient Reconstruction and Compression of Large Size ECG Signal by Tchebichef Moments,
ISCV20(1-6)
IEEE DOI 2011
data compression, electrocardiography, signal reconstruction, ECG signal, Thebichef moments, reconstruction methods, discrete orthogonal polynomials BibRef

Daoui, A., Karmouni, H., Azzayani, A., Sayyouri, M., Qjidaa, H.,
Large Size 1D Signal Analysis by Hybrid Tchebichef-Charlier Moments,
ISCV20(1-6)
IEEE DOI 2011
image reconstruction, image representation, polynomials, hybrid Tchebichef-Charlier moments, fast 1D signal reconstruction BibRef

Chen, H.[Hui], Wang, G.J.[Gui-Jin], Zhang, G.D.[Guo-Dong], Zhang, P.[Ping], Yang, H.Z.[Hua-Zhong],
CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification,
SPLetters(28), 2021, pp. 1993-1997.
IEEE DOI 2110
Electrocardiography, Training, Crops, Wavelet transforms, Task analysis, Signal processing algorithms, Rhythm, arrhythmia classification BibRef

Neeraj, Satija, U.[Udit], Mathew, J.[Jimson], Behera, R.K.,
A Unified Attentive Cycle-Generative Adversarial Framework for Deriving Electrocardiogram From Seismocardiogram Signal,
SPLetters(29), 2022, pp. 802-806.
IEEE DOI 2204
Electrocardiography, Generators, Databases, Training, Generative adversarial networks, Convolution, cycle-generative adversarial (CGAN) BibRef

Kim, H.[Hanvit], Phan, T.Q.[Thanh Quoc], Hong, W.[Wonjae], Chun, S.Y.[Se Young],
Physiology-based augmented deep neural network frameworks for ECG biometrics with short ECG pulses considering varying heart rates,
PRL(156), 2022, pp. 1-6.
Elsevier DOI 2205
Biometrics, Electrocardiogram, QT interval correction, Deep learning, Short ECG pulses BibRef

Wang, L.[Lu], Ohtsuki, T.[Tomoaki], Owada, K.[Kazunari], Honma, N.[Naoki], Hayashi, H.[Hayato],
A Multi-Layer Hybrid Network With Its Application in Fetal Heart Rate Monitoring,
SPLetters(29), 2022, pp. 1207-1211.
IEEE DOI 2206
Electrocardiography, Heart beat, Reservoirs, Morphology, Recording, Monitoring, Signal to noise ratio, Discriminator, multi-layer hybrid network BibRef

Jiang, X.J.[Xia-Jun], Toloubidokhti, M.[Maryam], Bergquist, J.A.[Jake A.], Zenger, B.[Brian], Good, W.W.[Wilson W.], MacLeod, R.S.[Rob S.], Wang, L.W.[Lin-Wei],
Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences,
MedImg(42), No. 2, February 2023, pp. 403-415.
IEEE DOI 2302
Geometry, Heart, Image reconstruction, Physics, Training data, Imaging, Torso, Geometric deep learning, inverse problems, physics-based deep learning BibRef

Toloubidokhti, M.[Maryam], Gyawali, P.K.[Prashnna K.], Gharbia, O.A.[Omar A.], Jiang, X.[Xiajun], Font, J.C.[Jaume Coll], Bergquist, J.A.[Jake A.], Zenger, B.[Brian], Good, W.W.[Wilson W.], Brooks, D.H.[Dana H.], MacLeod, R.S.[Rob S.], Wang, L.W.[Lin-Wei],
Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction,
FIMH21(471-481).
Springer DOI 2108
BibRef

Dissanayake, T.[Theekshana], Fernando, T.[Tharindu], Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
Multi-stage stacked temporal convolution neural networks (MS-S-TCNs) for biosignal segmentation and anomaly localization,
PR(139), 2023, pp. 109440.
Elsevier DOI 2304
Deep learning, Electrocardiogram, Heart sounds, Lung sounds, Segmentation, Model interpretation BibRef

Prabhakararao, E.[Eedara], Dandapat, S.[Samarendra],
Congestive Heart Failure Detection From ECG Signals Using Deep Residual Neural Network,
SMCS(53), No. 5, May 2023, pp. 3008-3018.
IEEE DOI 2305
Electrocardiography, Feature extraction, Recurrent neural networks, Heart beat, transparent diagnostic system BibRef

Gedon, D.[Daniel], Ribeiro, A.H.[Antônio H.], Wahlström, N.[Niklas], Schön, T.B.[Thomas B.],
Invertible Kernel PCA With Random Fourier Features,
SPLetters(30), 2023, pp. 563-567.
IEEE DOI 2305
Principal component analysis, Kernel, Image reconstruction, Dimensionality reduction, Noise reduction, Electrocardiography, reconstruction BibRef

Ekiz, D.[Deniz], Can, Y.S.[Yekta Said], Ersoy, C.[Cem],
Long Short-Term Memory Network Based Unobtrusive Workload Monitoring With Consumer Grade Smartwatches,
AffCom(14), No. 2, April 2023, pp. 895-905.
IEEE DOI 2306
Electrocardiography, Heart rate variability, Task analysis, Biomedical monitoring, Monitoring, Logic gates, Accelerometers, psychophysiology BibRef

Pelegrina, G.D.[Guilherme D.], Duarte, L.T.[Leonardo T.], Grabisch, M.[Michel],
Interpreting the Contribution of Sensors in Blind Source Extraction by Means of Shapley Values,
SPLetters(30), 2023, pp. 878-882.
IEEE DOI 2308
Sensors, Proposals, Games, Sensor arrays, Game theory, Electrocardiography, Data mining, Signal extraction, Shapley value BibRef

Ristov, S.[Sashko], Gusev, M.[Marjan], Hohenegger, A.[Armin], Prodan, R.[Radu], Mileski, D.[Dimitar], Gushev, P.[Pano], Temelkov, G.[Goran],
Serverless Electrocardiogram Stream Processing in Federated Clouds With Lambda Architecture,
Computer(56), No. 9, September 2023, pp. 18-27.
IEEE DOI 2309
BibRef

Donida-Labati, R.[Ruggero], Piuri, V.[Vincenzo], Rundo, F.[Francesco], Scotti, F.[Fabio],
MultiCardioNet: Interoperability between ECG and PPG biometrics,
PRL(175), 2023, pp. 1-7.
Elsevier DOI 2311
Biometrics, ECG, PPG, Interoperability, Siamese networks BibRef

Alizadeh, N., Afrakhteh, S., Mosavi, M.R.,
Deep CNN-based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes,
IJIST(33), No. 6, 2023, pp. 1988-2011.
DOI Link 2311
brain-computer interface, continuous wavelet transform filter bank, multiclass common spatial pattern BibRef

Yin, K.[Kang], Lim, E.Y.T.[Elissa Yan-Ting], Lee, S.W.[Seong-Whan],
GITGAN: Generative inter-subject transfer for EEG motor imagery analysis,
PR(146), 2024, pp. 110015.
Elsevier DOI Code:
WWW Link. 2311
BibRef
And: Corrigendum: PR(148), 2024, pp. 110217.
Elsevier DOI 2402
Brain-computer interface (BCI), Electroencephalogram (EEG), Unsupervised domain adaptation (UDA), Generative adversarial learning BibRef

Sharma, M.[Manish], Lodhi, H.[Harsh], Yadav, R.[Rishita], Acharya, U.R.[U. Rajendra],
Sleep disorder identification using wavelet scattering on ECG signals,
IJIST(34), No. 1, 2024, pp. e22980.
DOI Link 2401
electrocardiogram, ensemble of bagged trees, insomnia, machine learning, narcolepsy, nocturnal frontal lobe epilepsy, sleep disorder identification BibRef

Ahmed, A.F.[Anas Fouad],
An efficient wavelet thresholding strategy and robust shrinkage approach for de-noising ECG signal,
IJIST(34), No. 1, 2024, pp. e23009.
DOI Link 2401
de-noising, dynamic threshold, electrocardiogram, mother wavelet, shrinkage method BibRef

Wang, S.L.[Sheng-Lun], Ding, C.[Chun], Wang, Z.Z.[Zhao-Ze], Shen, L.[Lu], Wang, J.S.[Jun-Song],
Using normalized echo state network to detect abnormal ECG patterns,
IJIST(34), No. 1, 2024, pp. e22940.
DOI Link 2401
abnormal ECG patterns, ECG, normalized echo state network, reservoir parameters BibRef

Li, Q.[Qince], Liu, Y.[Yang], Zhang, Z.[Ze], Liu, J.[Jun], Yuan, Y.F.[Yong-Feng], Wang, K.Q.[Kuan-Quan], He, R.[Runnan],
Learning with incomplete labels of multisource datasets for ECG classification,
PR(150), 2024, pp. 110321.
Elsevier DOI 2403
Electrocardiogram, Multilabel classification, Incomplete labels, Multisource data mining BibRef

Alekhya, L.[Lanka], Kumar, P.R.[P. Rajesh],
A new approach to detect cardiovascular diseases using ECG scalograms and ML-based CNN algorithm,
IJCVR(14), No. 3, 2024, pp. 304-324.
DOI Link 2405
BibRef

Jiang, X.J.[Xia-Jun], Missel, R.[Ryan], Toloubidokhti, M.[Maryam], Gillette, K.[Karli], Prassl, A.J.[Anton J.], Plank, G.[Gernot], Horácek, B.M.[B. Milan], Sapp, J.L.[John L.], Wang, L.W.[Lin-Wei],
Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging,
MedImg(43), No. 8, August 2024, pp. 2733-2744.
IEEE DOI 2408
Electrocardiography, Bayes methods, Heart, Filtering, Data models, Image reconstruction, Physics, Image reconstruction, graph convolution BibRef

Shen, Y.T.[Yan-Ting], Lu, L.[Lei], Zhu, T.T.[Ting-Ting], Wang, X.S.[Xin-Shao], Clifton, L.[Lei], Chen, Z.M.[Zheng-Ming], Clarke, R.[Robert], Clifton, D.A.[David A.],
AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification,
PAMI(46), No. 10, October 2024, pp. 6542-6558.
IEEE DOI 2409
Training, Electrocardiography, Deep learning, Computer architecture, Medical services, Data models, electrocardiogram classification BibRef

Patro, K.K.[Kiran Kumar], Prakash, A.J.[Allam Jaya], Tummalapalli, G.[Geetamma], Kumari, P.L.[P. Lalitha], Rao, M.J.M.[M. Jaya Manmadha],
Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm,
IJCVR(15), No. 1, 2025, pp. 59-74.
DOI Link 2501
BibRef

Kucharski, D.[Damian], Wijata, A.M.[Agata M.], Fu, L.[Lu], Lin, W.D.[Wei-Dong], Xue, Y.[Yumei], Kawa, J.[Jacek], Zheng, Y.L.[Ya-Lin], Lip, Y.H.G.[Y. H. Gregory], Nalepa, J.[Jakub],
Giraffe: A Genetic Programming Algorithm To Build Deep Learning Ensembles for ECG Arrhythmia Classification,
ICIP24(3070-3076)
IEEE DOI 2411
Deep learning, Training, Analytical models, Arrhythmia, Genetic programming, Manuals, Electrocardiography, CVD BibRef

Nam, J.H.[Ju-Hyeon], Park, S.H.[Seo-Hyung], Kim, S.J.[Su Jung], Lee, S.C.[Sang-Chul],
Vizecgnet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation,
ICIP24(3219-3223)
IEEE DOI 2411
Training, Knowledge engineering, Heart, Visualization, Electrocardiography, Streaming media, Cardiovascular diseases, ECG Classification BibRef

Nguyen, V.D.[Vuong D.],
Fetal ECG Extraction on Time-Frequency Domain using Conditional GAN,
DEF-AI-MIA24(4943-4949)
IEEE DOI 2410
Time-frequency analysis, Visualization, Accuracy, Noise, Interference, Electrocardiography, Time-Frequency Domain BibRef

Akouz, N.[Nouhaila], El Ghazi, A.[Asmae], Moutaouakil, W.[Wassima], Hamida, S.[Soufiane], Cherradi, B.[Bouchaib], Raihani, A.[Abdelhadi],
Predicting Cardiovascular Disease: A Scoping Survey on different Datasets and DL/ML Models using ECG,
ISCV24(1-6)
IEEE DOI 2408
Support vector machines, Heart, Surveys, Reviews, Medical services, Electrocardiography, Predictive models, Deep Learning (DL) BibRef

Pais, D.[Daniela], Sebastião, R.[Raquel],
ECG Feature-based Classification of Induced Pain Levels,
CIARP23(II:45-59).
Springer DOI 2312
BibRef

Henriques, B.[Beatriz], Brás, S.[Susana], Gouveia, S.[Sónia],
Clustering ECG Time Series for the Quantification of Physiological Reactions to Emotional Stimuli,
IbPRIA23(680-692).
Springer DOI 2307
BibRef

Gu, K.[Kang], Prioleau, T.[Temiloluwa], Vosoughi, S.[Soroush],
Going Beyond Accuracy: Interpretability Metrics for CNN Representations of Physiological Signals,
ICPR22(4507-4513)
IEEE DOI 2212
Measurement, Deep learning, Visualization, Time series analysis, Neural networks, Electrocardiography, Physiology BibRef

Islam, M.F.[Md. Farhadul], Zabeen, S.[Sarah], Mehedi, M.H.K.[Md. Humaion Kabir], Iqbal, S.[Shadab], Rasel, A.A.[Annajiat Alim],
Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification,
SSSPR22(173-182).
Springer DOI 2301
BibRef

Sraitih, M.[Mohamed], Jabrane, Y.[Younes],
A survey of deep learning approaches for classifying ECG heartbeat arrhythmias,
ISCV22(1-8)
IEEE DOI 2208
Deep learning, Heart, Recurrent neural networks, Heart beat, Medical services, Electrocardiography, Multilayer perceptrons, Long Short-Term Memory. BibRef

Pais, D.[Daniela], Brás, S.[Susana], Sebastião, R.[Raquel],
Exploring Alterations in Electrocardiogram During the Postoperative Pain,
IbPRIA22(171-181).
Springer DOI 2205
BibRef

Gupta, P.[Priyanka], Bhaskarpandit, S.[Sathvik], Gupta, M.[Manik],
Similarity Learning based Few Shot Learning for ECG Time Series Classification,
DICTA21(1-8)
IEEE DOI 2201
Deep learning, Training, Databases, Digital images, Time series analysis, Euclidean distance, Electrocardiography BibRef

Wang, L.[Lei], Wang, Z.J.[Zhinuo J.], Doste, R.[Ruben], Santiago, A.[Alfonso], Zhou, X.[Xin], Quintanas, A.[Adria], Vazquez, M.[Mariano], Rodriguez, B.[Blanca],
Effects of Fibre Orientation on Electrocardiographic and Mechanical Functions in a Computational Human Biventricular Model,
FIMH21(351-361).
Springer DOI 2108
BibRef

Multerer, M.[Michael], Pezzuto, S.[Simone],
Fast and Accurate Uncertainty Quantification for the ECG with Random Electrodes Location,
FIMH21(561-572).
Springer DOI 2108
BibRef

Diallo, M.M.[Mohamadou Malal], Coudière, Y.[Yves], Dubois, R.[Rémi],
A Volume Source Method for Solving ECGI Inverse Problem,
FIMH21(551-560).
Springer DOI 2108
BibRef

Tate, J.D.[Jess D.], Good, W.W.[Wilson W.], Zemzemi, N.[Nejib], Boonstra, M.[Machteld], van Dam, P.[Peter], Brooks, D.H.[Dana H.], Narayan, A.[Akil], MacLeod, R.S.[Rob S.],
Uncertainty Quantification of the Effects of Segmentation Variability in ECGI,
FIMH21(515-522).
Springer DOI 2108
BibRef

Ogiermann, D.[Dennis], Balzani, D.[Daniel], Perotti, L.E.[Luigi E.],
The Effect of Modeling Assumptions on the ECG in Monodomain and Bidomain Simulations,
FIMH21(503-514).
Springer DOI 2108
BibRef

Bergquist, J.A.[Jake A.], Coll-Font, J.[Jaume], Zenger, B.[Brian], Rupp, L.C.[Lindsay C.], Good, W.W.[Wilson W.], Brooks, D.H.[Dana H.], MacLeod, R.S.[Rob S.],
Simultaneous Multi-heartbeat ECGI Solution with a Time-Varying Forward Model: A Joint Inverse Formulation,
FIMH21(493-502).
Springer DOI 2108
BibRef

Kashtanova, V.[Victoriya], Ayed, I.[Ibrahim], Cedilnik, N.[Nicolas], Gallinari, P.[Patrick], Sermesant, M.[Maxime],
EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology,
FIMH21(482-492).
Springer DOI 2108
BibRef

Gassa, N.[Narimane], Zemzemi, N.[Nejib], Corrado, C.[Cesare], Coudière, Y.[Yves],
Spiral Waves Generation Using an Eikonal-Reaction Cardiac Electrophysiology Model,
FIMH21(523-530).
Springer DOI 2108
BibRef

Kharche, S.R.[Sanjay R.], Mironova, G.Y.[Galina Yu.], Goldman, D.[Daniel], McIntyre, C.W.[Christopher W.], Welsh, D.G.[Donald G.],
Sensitivity Analysis of a Smooth Muscle Cell Electrophysiological Model,
FIMH21(540-550).
Springer DOI 2108
BibRef

Zhang, H.J.[Hao-Jie], Yang, G.P.[Gong-Ping], Huang, Y.[Yuwen], Yuan, F.[Feng], Yin, Y.L.[Yi-Long],
Multi-Scale and Attention based ResNet for Heartbeat Classification,
ICPR21(1529-1535)
IEEE DOI 2105
Pregnancy, Deep learning, Heart beat, Convolution, Electrocardiography, Benchmark testing, Feature extraction BibRef

Rodrigues, T.[Tiago], Samoutphonh, S.[Sirisack], Silva, H.[Hugo], Fred, A.[Ana],
A Low-Complexity R-peak Detection Algorithm with Adaptive Thresholding for Wearable Devices,
ICPR21(1-8)
IEEE DOI 2105
Heart rate, Sensitivity, Databases, Wearable computers, Detectors, Electrocardiography, Real-time systems BibRef

Dasgupta, S.[Subhrajyoti], Das, S.[Sudip], Bhattacharya, U.[Ujjwal],
CardioGAN: An Attention-based Generative Adversarial Network for Generation of Electrocardiograms,
ICPR21(3193-3200)
IEEE DOI 2105
Training, Heart, Measurement, Privacy, Data privacy, Machine learning, Electrocardiography BibRef

Han, C.Q.[Chuan-Qi], Huang, R.[Ruoran], Yu, F.[Fang], Huang, X.[Xi], Cui, L.[Li],
EasiECG: A Novel Inter-Patient Arrhythmia Classification Method using ECG Waves,
ICPR21(1-8)
IEEE DOI 2105
Pregnancy, Adaptation models, Heart beat, Databases, Convolution, Linear regression, Electrocardiography BibRef

Bitetto, A.[Alessandro], Bianchi, E.[Elena], Dondi, P.[Piercarlo], Bianchi, L.[Luca], Tolgyesi, J.[Janos], Ferri, D.[Diego], Lombardi, L.[Luca], Cerchiello, P.[Paola], Marceca, A.[Azzurra], Barosi, A.[Alberto],
Deep Learning Detection of Cardiac Akinesis in Echocardiograms,
AIHA20(503-514).
Springer DOI 2103
BibRef

Tate, J.D.[Jess D.], Schuler, S.[Steffen], Dössel, O.[Olaf], MacLeod, R.S.[Robert S.], Oostendorp, T.F.[Thom F.],
Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations,
FIMH19(147-155).
Springer DOI 1906
BibRef

Schuler, S.[Steffen], Tate, J.D.[Jess D.], Oostendorp, T.F.[Thom F.], MacLeod, R.S.[Robert S.], Dössel, O.[Olaf],
Spatial Downsampling of Surface Sources in the Forward Problem of Electrocardiography,
FIMH19(29-36).
Springer DOI 1906
BibRef

Karoui, A.[Amel], Bendahmane, M.[Mostafa], Zemzemi, N.[Nejib],
A Spatial Adaptation of the Time Delay Neural Network for Solving ECGI Inverse Problem,
FIMH19(94-102).
Springer DOI 1906
BibRef

Rababah, A.[Ali], Finlay, D.[Dewar], Bear, L.[Laura], Bond, R.[Raymond], Rjoob, K.[Khaled], Mclaughlin, J.[James],
Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse Reconstruction of Cardiac Electrical Activity,
FIMH19(112-120).
Springer DOI 1906
BibRef

Abdeldayem, S.S., Bourlai, T.,
Automatically Detecting Arrhythmia-related Irregular Patterns using the Temporal and Spectro-Temporal Textures of ECG Signals,
ICPR18(2301-2307)
IEEE DOI 1812
Electrocardiography, Feature extraction, Heart beat, Time-frequency analysis, Medical services, Wavelet transforms BibRef

Wang, H.,
ReNN: Rule-embedded Neural Networks,
ICPR18(824-829)
IEEE DOI 1812
Artificial neural networks, Electrocardiography, Feature extraction, Heart rate, Computational modeling, interpretability BibRef

El Bouny, L., Khalil, M., Adib, A.,
Performance analysis of ECG signal denoising methods in transform domain,
ISCV18(1-8)
IEEE DOI 1807
AWGN, discrete wavelet transforms, electrocardiography, mean square error methods, medical signal processing, Thresholding BibRef