Multi-task cascaded assessment of signal quality for long-term single-lead ECG monitoring
暂无分享,去创建一个
[1] Cuiwei Yang,et al. MGNN: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection , 2022, Comput. Biol. Medicine.
[2] Lan Tian,et al. ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features , 2021, Comput. Methods Programs Biomed..
[3] Indrajeet Patil,et al. Visualizations with statistical details: The 'ggstatsplot' approach , 2021, J. Open Source Softw..
[4] Vaidotas Marozas,et al. Considerations on Performance Evaluation of Atrial Fibrillation Detectors , 2021, IEEE Transactions on Biomedical Engineering.
[5] P. Noseworthy,et al. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management , 2021, Nature Reviews Cardiology.
[6] Panos Vardas,et al. European Society of Cardiology: Cardiovascular Disease Statistics 2019. , 2019, European heart journal.
[7] Qifei Zhang,et al. A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG , 2019, Comput. Math. Methods Medicine.
[8] Sabine Van Huffel,et al. Artefact detection and quality assessment of ambulatory ECG signals , 2019, Comput. Methods Programs Biomed..
[9] Cuiwei Yang,et al. A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[10] Xiangyu Zhang,et al. Signal Quality Assessment and Lightweight QRS Detection for Wearable ECG SmartVest System , 2019, IEEE Internet of Things Journal.
[11] Monika Mittal,et al. ECG Signal Analysis: Past, Present and Future , 2018, 2018 IEEE 8th Power India International Conference (PIICON).
[12] Negin Yaghmaie,et al. Dynamic signal quality index for electrocardiograms , 2018, Physiological measurement.
[13] João Paulo Silva Cunha,et al. Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies , 2018, Sensors.
[14] Selcan Kaplan Berkaya,et al. A survey on ECG analysis , 2018, Biomed. Signal Process. Control..
[15] M. Sabarimalai Manikandan,et al. Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring , 2018, IEEE Journal of Biomedical and Health Informatics.
[16] M. Sabarimalai Manikandan,et al. A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment , 2018, IEEE Reviews in Biomedical Engineering.
[17] Michael B. Gravenor,et al. Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation: The REHEARSE-AF Study , 2017, Circulation.
[18] Vaidotas Marozas,et al. Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes , 2017, Physiological measurement.
[19] Adrian D. C. Chan,et al. Signal Quality Analysis of Ambulatory Electrocardiograms to Gate False Myocardial Ischemia Alarms , 2017, IEEE Transactions on Biomedical Engineering.
[20] Nigel H. Lovell,et al. QRS Detection Algorithm for Telehealth Electrocardiogram Recordings , 2016, IEEE Transactions on Biomedical Engineering.
[21] M. Sabarimalai Manikandan,et al. A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Gari D. Clifford,et al. A machine learning approach to multi-level ECG signal quality classification , 2014, Comput. Methods Programs Biomed..
[23] Mohamed Elgendi,et al. Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases , 2013, PloS one.
[24] M. McHugh. Interrater reliability: the kappa statistic , 2012, Biochemia medica.
[25] N H Lovell,et al. Electrocardiogram signal quality measures for unsupervised telehealth environments , 2012, Physiological measurement.
[26] G D Clifford,et al. Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms , 2012, Physiological measurement.
[27] Joon Lee,et al. Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings , 2012, IEEE Transactions on Biomedical Engineering.
[28] Xiaopeng Zhao,et al. Computer algorithms for evaluating the quality of ECGs in real time , 2011, 2011 Computing in Cardiology.
[29] Dieter Hayn,et al. ECG quality assessment for patient empowerment in mHealth applications , 2011, 2011 Computing in Cardiology.
[30] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[31] M Vaglio,et al. Use of ECG quality metrics in clinical trials , 2010, 2010 Computing in Cardiology.
[32] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[34] M. Chambrin. Alarms in the intensive care unit: how can the number of false alarms be reduced? , 2001, Critical care.
[35] G.B. Moody,et al. The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.
[36] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[37] J. Richman,et al. Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.
[38] Willis J. Tompkins,et al. A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.
[39] Vladimir Vapnik,et al. Support-vector networks , 2004, Machine Learning.
[40] L. Breiman. Random Forests , 2001, Machine Learning.
[41] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.