Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder
暂无分享,去创建一个
Maysam F. Abbod | Jiann-Shing Shieh | Bhekumuzi Mathunjwa | Muammar Sadrawi | Shou-Zen Fan | Yin-Tsong Lin | Chien-Hung Lin | Ho-Tsung Hsin | J. Shieh | M. Abbod | S. Fan | Muammar Sadrawi | Yin-Tsong Lin | H. Hsin | Chien-Hung Lin | Bhekumuzi Mathunjwa | Bhekumuzi M. Mathunjwa
[1] Jiann-Shing Shieh,et al. Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography , 2020, Sensors.
[2] B. Gerber,et al. Electrocardiographic changes after head trauma. , 2005, Journal of Electrocardiology.
[3] P. Podolec,et al. ECG Markers of Hemodynamic Improvement in Patients with Pulmonary Hypertension , 2018, BioMed research international.
[4] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[5] Xiaomao Fan,et al. An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals , 2021, Sensors.
[6] J. Thiran,et al. Non-invasive pulmonary artery pressure estimation by electrical impedance tomography in a controlled hypoxemia study in healthy subjects , 2020, Scientific Reports.
[7] S. Meek,et al. Introduction. I—Leads, rate, rhythm, and cardiac axis , 2002, BMJ : British Medical Journal.
[8] Maysam F. Abbod,et al. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks , 2015, BioMed research international.
[9] Thomas Heldt,et al. Pseudo-Bayesian Model-Based Noninvasive Intracranial Pressure Estimation and Tracking , 2020, IEEE Transactions on Biomedical Engineering.
[10] Bob D. de Vos,et al. State-of-the-Art Deep Learning in Cardiovascular Image Analysis. , 2019, JACC. Cardiovascular imaging.
[11] E. Frohlich,et al. Electrocardiographic Changes Reflecting Left Atrial Abnormality in Hypertension , 1966, Circulation.
[12] R. Kalbag,et al. Electrocardiographic abnormalities associated with raised intracranial pressure. , 1975, British medical journal.
[13] Md. Kamrul Hasan,et al. Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network , 2018, Biomed. Signal Process. Control..
[14] Zhong Ji,et al. Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography , 2019, Sensors.
[15] S. L. Miller,et al. Traumatic Subarachnoid Hemorrhage and QTc Prolongation , 2004, Journal of neurosurgical anesthesiology.
[16] W. Craelius,et al. Trending autoregulatory indices during treatment for traumatic brain injury , 2016, Journal of Clinical Monitoring and Computing.
[17] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[18] F Golliot,et al. Complications of femoral and subclavian venous catheterization in critically ill patients: a randomized controlled trial. , 2001, JAMA.
[19] Majid Sarrafzadeh,et al. Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).
[20] Thomas Heldt,et al. A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation , 2019, IEEE Journal of Biomedical and Health Informatics.
[21] A. V. van Vliet,et al. Hypertensive crisis-induced electrocardiographic changes: a case series , 2009, Journal of medical case reports.
[22] Edith Grall-Maës,et al. Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism , 2021, Sensors.
[23] G. Ross. EFFECT OF HYPERTENSION ON THE P WAVE OF THE ELECTROCARDIOGRAM , 1963, British heart journal.
[24] Mitja Lustrek,et al. Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network , 2019, Sensors.
[25] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[26] Maysam F. Abbod,et al. HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia , 2017, PeerJ.
[27] S. Chatterjee. ECG Changes in Subarachnoid Haemorrhage: A Synopsis , 2011, Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation.
[28] Sabee Molloi,et al. Detecting Cardiovascular Disease from Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.
[29] Mark Butlin,et al. Arterial blood pressure measurement and pulse wave analysis—their role in enhancing cardiovascular assessment , 2010, Physiological measurement.
[30] Maysam F. Abbod,et al. Arrhythmia Evaluation in Wearable ECG Devices , 2017, Sensors.
[31] Cheolsoo Park,et al. End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism , 2020, Sensors.
[32] B. Hedblad,et al. Hypertension and ST segment depression during ambulatory electrocardiographic recording. Results from the prospective population study 'men born in 1914' from Malmö, Sweden. , 1992, Hypertension.
[33] M. Matucci-Cerinic,et al. Use of ECG and Other Simple Non-Invasive Tools to Assess Pulmonary Hypertension , 2016, PloS one.
[34] Maysam F. Abbod,et al. Intermittent blood pressure prediction via multiscale entropy and ensemble artificial neural networks , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).
[35] Maysam F. Abbod,et al. Ensemble Genetic Fuzzy Neuro Model Applied for the Emergency Medical Service via Unbalanced Data Evaluation , 2018, Symmetry.
[36] J. van der Naalt,et al. The Association of Early Electrocardiographic Abnormalities With Brain Injury Severity and Outcome in Severe Traumatic Brain Injury , 2021, Frontiers in Neurology.
[37] Thomas Penzel,et al. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea , 2003, IEEE Transactions on Biomedical Engineering.
[38] Sabine Van Huffel,et al. A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG , 2015, IEEE Transactions on Biomedical Engineering.
[39] Ki H. Chon,et al. A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor , 2015, Sensors.
[40] Rong Zhang,et al. Arterial blood pressure feature estimation using photoplethysmography , 2018, Comput. Biol. Medicine.
[41] Mohammad Monir Uddin,et al. Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques , 2020, Sensors.
[42] T. Penzel,et al. Dynamics of Heart Rate and Sleep Stages in Normals and Patients with Sleep Apnea , 2003, Neuropsychopharmacology.
[43] U. Baumann,et al. Noninvasive central venous pressure measurement by controlled compression sonography at the forearm. , 2007, Journal of the American College of Cardiology.
[44] Antonello Rizzi,et al. Cancer Diagnosis Using Deep Learning: A Bibliographic Review , 2019, Cancers.
[45] J. Gummert,et al. Non-invasive assessment of central venous pressure in heart failure: a systematic prospective comparison of echocardiography and Swan-Ganz catheter , 2020, The International Journal of Cardiovascular Imaging.
[46] Maysam F. Abbod,et al. Ensemble empirical mode decomposition applied for PPG motion artifact , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).
[47] C. Hersch,et al. Electrocardiographic Changes in Head Injuries , 1961, Circulation.
[48] R. Baranowski,et al. J-wave formation in patients with acute intracranial hypertension. , 2009, Journal of electrocardiology.
[49] H. Snieder,et al. Causal Pathways from Blood Pressure to Larger QRS Amplitudes: a Mendelian Randomization Study , 2018, Scientific Reports.
[50] U. Rajendra Acharya,et al. Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank , 2020, Comput. Biol. Medicine.
[51] Lori Shutter,et al. A trial of intracranial pressure monitoring in traumatic brain injury , 2014, Critical Care.