Search Results - "Gliner, Vadim"

Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms by Gliner, Vadim, Keidar, Noam, Makarov, Vladimir, Avetisyan, Arutyun I., Schuster, Assaf, Yaniv, Yael

“…Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG…”
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Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms by Gliner, Vadim, Makarov, Vladimir, Avetisyan, Arutyun I., Schuster, Assaf, Yaniv, Yael

“…12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may…”
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Identification of features for machine learning analysis for automatic arrhythmogenic event classification by Gliner, Vadim, Yaniv, Yael

“…Cardiac arrhythmias are the leading cause of death in the western world, where atrial fibrillation (AF) is the most common arrhythmias. The PhysioNet/CinC 2017…”
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Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector by Gliner, Vadim, Behar, Joachim, Yaniv, Yael

“…In parallel to the introduction of mobile communication devices with high computational power and internet connectivity, high-quality and low-cost health…”
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PO-635-01 REDUCTION OF FAR-FIELD POTENTIALS USING CLOSE-UNIPOLAR ELECTROGRAMS IN A NOVEL ULTRA-HIGH RESOLUTION CATHETER MAPPING SYSTEM: EVALUATION IN A SWINE ATRIAL LINEAR LESION MODEL by An, Yoshimori, Nakagawa, Hiroshi, Kuroda, Shunsuke, Govari, Assaf, Beeckler, Christopher T., Gliner, Vadim, Keyes, Joe T., Herrera, Kevin J., Ikeda, Atsushi, Nakhla, Shady, Hussein, Ayman A., Saliba, Walid I., Wazni, Oussama M., Jackman, Warren M.

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B-PO04-081 IMPROVED ACCURACY OF COMPLEX ATRIAL MAPPING USING CLOSE-UNIPOLAR ELECTROGRAMS IN A NOVEL ULTRA-HIGH RESOLUTION CATHETER MAPPING SYSTEM by Nakagawa, Hiroshi, Kuroda, Shunsuke, Govari, Assaf, Beeckler, Christopher T., Gliner, Vadim, Zliberman, Israel, Keyes, Joseph T., Herrera, Kevin, Ikeda, Atsushi, Hussein, Ayman A., Saliba, Walid I., Wazni, Oussama M., Jackman, Warren M.

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Assessment of the impact of non-architectural changes in the predictive model on the quality of ECG classification by Ananev, Vladislav Valerievich, Skorik, Sergej Nikolaevich, Shaklein, Vsevolod Vladislavovich, Avetisyan, Aram Arutyunovich, Teregulov, Yurij Emilevich, Turdakov, Denis Yuryevich, Gliner, Vadim, Schuster, Assaf, Karpulevich, Evgeny Andreevich

“…Recording and analyzing 12-lead electrocardiograms is the most common procedure for detecting heart disease. Recently, various deep learning methods have been…”
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Non-architectural improvements for ECG classification using deep neural network by Andreev, Pavel, Ananev, Vladislav, Avetisyan, Aram, Makarov, Vladimir, Gliner, Vadim, Schuster, Assaf, Karpulevich, Evgeny

“…Due to the latest advances in machine learning algorithms new deep learning-based approaches to the interpretation of 12-lead electrocardiograms have been…”
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