Every reference with a DOI in the deposited reference list resolved to a known
work in Crossref or DataCite at the dated check, and none carried a retraction,
withdrawal, or removal notice.
The 49 checked references that resolve
resolves10.3390/jcm8010036Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some–Different–Light into the Dark
resolves10.1007/s11947-011-0725-1Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment
resolves10.1016/j.rti.2005.04.003Industrial application for inline material sorting using hyperspectral imaging in the NIR range
resolves10.1002/ett.3611Single image defocus estimation by modified gaussian function
resolves10.1155/2018/7310496Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants
resolves10.1109/10.740880Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network
resolves10.1109/10.58593An approach to cardiac arrhythmia analysis using hidden Markov models
resolves10.1109/TBME.2006.880879Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features
resolves10.1109/10.623058A patient-adaptable ECG beat classifier using a mixture of experts approach
resolves10.1016/j.eswa.2006.05.014Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network
resolves10.1006/jbin.2001.1004A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions
resolves10.1007/s11227-018-2263-3A machine learning approach for feature selection traffic classification using security analysis
resolves10.1002/ett.3627An optimal multitier resource allocation of cloud RAN in 5G using machine learning
resolves10.1007/s00259-014-2882-8Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [123I]FP-CIT SPECT
resolves10.3389/fnins.2015.00307Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
resolves10.1109/ACCESS.2019.2937875Smart Heart Monitoring: Early Prediction of Heart Problems Through Predictive Analysis of ECG Signals
resolves10.22489/CinC.2017.066-138Robust ECG Signal Classification for the Detection of Atrial Fibrillation Using Novel Neural Networks
resolves10.1016/j.dsp.2008.09.002Combining recurrent neural networks with eigenvector methods for classification of ECG beats
resolves10.1016/j.medengphy.2010.08.007Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification
resolves10.1109/TITB.2008.923147Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization
The 12 references without a DOI — listed, not checked
no DOI — not checkedCardiovascular disease as a leading cause of death: How are pharmacists getting involved?
no DOI — not checkedWu, Y., Yang, F., Liu, Y., Zha, X., and Yuan, S. (2018). A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification. arXiv.
no DOI — not checkedApplications of artificial neural networks for ECG signal detection and classification
no DOI — not checkedNovel ECG diagnosis model based on multi-stage artificial neural networks
no DOI — not checkedEcar, A. (1987). Recommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithms. Assoc. Adv. Med. Instrum., 69.
no DOI — not checkedRajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv.
no DOI — not checkedLee, S.C. (1990). Using a translation-invariant neural network to diagnose heart arrhythmia. Advances in Neural Information Processing Systems, Morgan Kaufmann.
no DOI — not checkedA patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features
no DOI — not checkedClevert, D.A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv.
no DOI — not checkedJun, T.J., Nguyen, H.M., Kang, D., Kim, D., Kim, D., and Kim, Y.H. (2018). ECG arrhythmia classification using a 2-D convolutional neural network. arXiv.
no DOI — not checkedAbadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv.
no DOI — not checkedInvestigating cardiac arrhythmia in ECG using random forest classification
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