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Items: 1 to 20 of 99

1.

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

Shi H, Wang H, Huang Y, Zhao L, Qin C, Liu C.

Comput Methods Programs Biomed. 2019 Apr;171:1-10. doi: 10.1016/j.cmpb.2019.02.005. Epub 2019 Feb 20.

PMID:
30902245
2.

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

Huang H, Liu J, Zhu Q, Wang R, Hu G.

Biomed Eng Online. 2014 Jun 5;13:72. doi: 10.1186/1475-925X-13-72.

3.

Automated real-time method for ventricular heartbeat classification.

Ortín S, Soriano MC, Alfaras M, Mirasso CR.

Comput Methods Programs Biomed. 2019 Feb;169:1-8. doi: 10.1016/j.cmpb.2018.11.005. Epub 2018 Nov 20.

PMID:
30638588
4.

Heartbeat classification using disease-specific feature selection.

Zhang Z, Dong J, Luo X, Choi KS, Wu X.

Comput Biol Med. 2014 Mar;46:79-89. doi: 10.1016/j.compbiomed.2013.11.019. Epub 2013 Dec 3.

PMID:
24529208
5.

Patient-specific ECG beat classification technique.

Das MK, Ari S.

Healthc Technol Lett. 2014 Sep 26;1(3):98-103. doi: 10.1049/htl.2014.0072. eCollection 2014 Sep.

6.

Automatic classification of heartbeats using ECG morphology and heartbeat interval features.

de Chazal P, O'Dwyer M, Reilly RB.

IEEE Trans Biomed Eng. 2004 Jul;51(7):1196-206.

PMID:
15248536
7.

ECG Beats Classification Using Mixture of Features.

Das MK, Ari S.

Int Sch Res Notices. 2014 Sep 17;2014:178436. doi: 10.1155/2014/178436. eCollection 2014.

8.

A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features.

de Chazal P, Reilly RB.

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 1):2535-43.

PMID:
17153211
9.

Towards End-to-End ECG Classification with Raw Signal Extraction and Deep Neural Networks.

Xu SS, Mak MW, Cheung CC.

IEEE J Biomed Health Inform. 2018 Sep 20. doi: 10.1109/JBHI.2018.2871510. [Epub ahead of print]

PMID:
30235153
10.

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals.

Huang H, Liu J, Zhu Q, Wang R, Hu G.

Biomed Eng Online. 2014 Jun 30;13:90. doi: 10.1186/1475-925X-13-90.

11.

A novel application of deep learning for single-lead ECG classification.

Mathews SM, Kambhamettu C, Barner KE.

Comput Biol Med. 2018 Aug 1;99:53-62. doi: 10.1016/j.compbiomed.2018.05.013. Epub 2018 Jun 4.

PMID:
29886261
12.

Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO.

Garcia G, Moreira G, Menotti D, Luz E.

Sci Rep. 2017 Sep 5;7(1):10543. doi: 10.1038/s41598-017-09837-3.

13.

A deep convolutional neural network model to classify heartbeats.

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS.

Comput Biol Med. 2017 Oct 1;89:389-396. doi: 10.1016/j.compbiomed.2017.08.022. Epub 2017 Aug 24.

PMID:
28869899
14.

Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis.

Zhu W, Chen X, Wang Y, Wang L.

IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):131-138. doi: 10.1109/TCBB.2018.2846611. Epub 2018 Jun 12.

PMID:
29994263
15.

Identifying Hypertrophic Cardiomyopathy Patients by Classifying Individual Heartbeats from 12-lead ECG Signals.

Rahman QA, Tereshchenko LG, Kongkatong M, Abraham T, Abraham MR, Shatkay H.

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2014 Nov;2014:224-229.

16.

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T.

Comput Methods Programs Biomed. 2016 Apr;127:52-63. doi: 10.1016/j.cmpb.2015.12.024. Epub 2016 Jan 20.

PMID:
27000289
17.

Detection of premature ventricular contractions on a ventricular electrocardiogram for patients with left ventricular assist devices.

Park SM, Lee JH, Choi SW.

Artif Organs. 2014 Dec;38(12):1040-6. doi: 10.1111/aor.12306. Epub 2014 Apr 21.

PMID:
24749943
18.
19.

Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification.

Christov I, Gómez-Herrero G, Krasteva V, Jekova I, Gotchev A, Egiazarian K.

Med Eng Phys. 2006 Nov;28(9):876-87. Epub 2006 Feb 14.

PMID:
16476566
20.

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, Dubsky P, Baltzer P, Clauser P, Kapetas P, Morris EA, Meyer-Baese A, Pinker K.

Invest Radiol. 2019 Feb;54(2):110-117. doi: 10.1097/RLI.0000000000000518.

PMID:
30358693

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