|
[1]
|
Discrimination of Ventricular Arrhythmias using Machine Learning models
2023 10th International Conference on Signal Processing and Integrated Networks (SPIN),
2023
DOI:10.1109/SPIN57001.2023.10116579
|
|
|
|
|
[2]
|
On Database, Segmentation and Classifier Influence in Ventricular Fibrillation Detection
2023 International Symposium on Signals, Circuits and Systems (ISSCS),
2023
DOI:10.1109/ISSCS58449.2023.10190962
|
|
|
|
|
[3]
|
Shockable Arrhythmia Detection Using Sequential Minimal Optimization, and Support Vector Machine
2023 Innovations in Power and Advanced Computing Technologies (i-PACT),
2023
DOI:10.1109/i-PACT58649.2023.10434457
|
|
|
|
|
[4]
|
Analysis of The Detection of Ventricular Fibrillation in Its First 3 Seconds Using Different Features and Classifiers
2022 E-Health and Bioengineering Conference (EHB),
2022
DOI:10.1109/EHB55594.2022.9991738
|
|
|
|
|
[5]
|
Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model
Biocybernetics and Biomedical Engineering,
2022
DOI:10.1016/j.bbe.2022.02.006
|
|
|
|
|
[6]
|
Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach
2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP),
2022
DOI:10.1109/ICICCSP53532.2022.9862477
|
|
|
|
|
[7]
|
A review of progress and an advanced method for shock advice algorithms in automated external defibrillators
BioMedical Engineering OnLine,
2022
DOI:10.1186/s12938-022-00993-w
|
|
|
|
|
[8]
|
Analysis of The Detection of Ventricular Fibrillation in Its First 3 Seconds Using Different Features and Classifiers
2022 E-Health and Bioengineering Conference (EHB),
2022
DOI:10.1109/EHB55594.2022.9991738
|
|
|
|
|
[9]
|
Large group activity security risk assessment and risk early warning based on random forest algorithm
Pattern Recognition Letters,
2021
DOI:10.1016/j.patrec.2021.01.008
|
|
|
|
|
[10]
|
Large group activity security risk assessment and risk early warning based on random forest algorithm
Pattern Recognition Letters,
2021
DOI:10.1016/j.patrec.2021.01.008
|
|
|
|
|
[11]
|
Discrimination of Life-Threatening Arrhythmias Using Singular Value, Harmonic Phase Distribution, and Dynamic Time Warping of ECG Signals
IEEE Transactions on Instrumentation and Measurement,
2021
DOI:10.1109/TIM.2020.3045190
|
|
|
|
|
[12]
|
Recognition of Dangerous Rhythm Disturbances from Short ECG Fragments
2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT),
2021
DOI:10.1109/USBEREIT51232.2021.9455071
|
|
|
|
|
[13]
|
ECG Database for Evaluating the Efficiency of Recognizing Dangerous Arrhythmias
2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB),
2021
DOI:10.1109/CSGB53040.2021.9496029
|
|
|
|
|
[14]
|
Intelligent and Efficient Detection of Life-Threatening Ventricular Arrhythmias in Short Segments of Surface ECG Signals
IEEE Sensors Journal,
2021
DOI:10.1109/JSEN.2020.3031597
|
|
|
|
|
[15]
|
Machine learning-data mining integrated approach for premature ventricular contraction prediction
Neural Computing and Applications,
2021
DOI:10.1007/s00521-021-05820-2
|
|
|
|
|
[16]
|
DETECTION OF VENTRICULAR FIBRILLATION USING WAVELET TRANSFORM AND PHASE SPACE RECONSTRUCTION FROM ECG SIGNALS
Journal of Mechanics in Medicine and Biology,
2021
DOI:10.1142/S0219519421400364
|
|
|
|
|
[17]
|
Deep Neural Network Approach for Continuous ECG‐Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation
Journal of the American Heart Association,
2021
DOI:10.1161/JAHA.120.019065
|
|
|
|
|
[18]
|
Recognition of the Life-Threatening Cardiac Arrhythmias in the Frequency Domain
2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus),
2020
DOI:10.1109/EIConRus49466.2020.9039134
|
|
|
|
|
[19]
|
VFPred: A fusion of signal processing and machine learning techniques in detecting ventricular fibrillation from ECG signals
Biomedical Signal Processing and Control,
2019
DOI:10.1016/j.bspc.2018.12.016
|
|
|
|
|
[20]
|
Intelligent Analysis of Biomedical Signals for Personal Identification and Life Support Systems
Procedia Computer Science,
2019
DOI:10.1016/j.procs.2019.02.022
|
|
|
|
|
[21]
|
Recognition of Arrhythmias Based on the Spectral Description of ECG
2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT),
2019
DOI:10.1109/USBEREIT.2019.8736628
|
|
|
|
|
[22]
|
Bioelectronics and Medical Devices
2019
DOI:10.1016/B978-0-08-102420-1.00042-X
|
|
|
|
|
[23]
|
VT/VF Detection Method Based On ECG Signal Quality Assessment
Journal of Circuits, Systems and Computers,
2018
DOI:10.1142/S0218126618501694
|
|
|
|
|
[24]
|
Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals
Sensors,
2018
DOI:10.3390/s18072090
|
|
|
|
|
[25]
|
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
Applied Sciences,
2018
DOI:10.3390/app8112057
|
|
|
|