Cardiac Arrest — Development of a Novel Convolution Neural Network for Arrhythmia Classification
Citation(s)
Arvanaghi R, Daneshvar S, Seyedarabi H, Goshvarpour A Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification. Comput Methods Programs Biomed. 2017 Nov;151:71-78. doi: 10.1016/j.cmpb.2017.08.013. Epub 2017 Aug 24.
Bhavnani SP, Narula J, Sengupta PP Mobile technology and the digitization of healthcare. Eur Heart J. 2016 May 7;37(18):1428-38. doi: 10.1093/eurheartj/ehv770. Epub 2016 Feb 11. Review.
Fan X, Yao Q, Cai Y, Miao F, Sun F, Li Y Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings. IEEE J Biomed Health Inform. 2018 Nov;22(6):1744-1753. doi: 10.1109/JBHI.2018.28587
Kiranyaz S, Ince T, Gabbouj M Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Trans Biomed Eng. 2016 Mar;63(3):664-75. doi: 10.1109/TBME.2015.2468589. Epub 2015 Aug 14.
Li Q, Rajagopalan C, Clifford GD Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng. 2014 Jun;61(6):1607-13. doi: 10.1109/TBME.2013.2275000. Epub 2013 Jul 26.
Lyon A, Mincholé A, Martínez JP, Laguna P, Rodriguez B Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface. 2018 Jan;15(138). pii: 20170821. doi: 10.1098/rsif.2017.0821. Revie
Vandendriessche B, Abas M, Dick TE, Loparo KA, Jacono FJ A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features. IEEE Trans Biomed Eng. 2017 Dec;64(12):2890-2900. doi: 10.1109/TBME.2017.2684244. Epub 2017 Mar 17.
Warrick PA, Nabhan Homsi M Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection. Physiol Meas. 2018 Oct 30;39(11):114002. doi: 10.1088/1361-6579/aad386.
Xiong Z, Nash MP, Cheng E, Fedorov VV, Stiles MK, Zhao J ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol Meas. 2018 Sep 24;39(9):094006. doi: 10.1088/1361-6579/aad9ed.
Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms
Interventional studies are often prospective and are specifically tailored to evaluate direct impacts of treatment or preventive measures on disease.
Observational studies are often retrospective and are used to assess potential causation in exposure-outcome relationships and therefore influence preventive methods.
Expanded access is a means by which manufacturers make investigational new drugs available, under certain circumstances, to treat a patient(s) with a serious disease or condition who cannot participate in a controlled clinical trial.
Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.
Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.
