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Category:All HeadlinesWorld & GeopoliticsMarkets & EconomyTech & AIPoliticsSearch results for: "Deep learning model using ECGs" (30 stories)

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Deep learning model using ECGs during sleep studies can predict cardiovascular outcomes
Lead StoryNational Institutes of Health (NIH) | (.gov)general
1d ago

Deep learning model using ECGs during sleep studies can predict cardiovascular outcomes

<a href="https://news.google.com/rss/articles/CBMizgFBVV95cUxNQjJBQkdTenlWbVZFSjdSOHdsSWJ6anZ4OGxleElub0FEaVh5dXk2TmpWWThubGxrWmI3ZDFVcUNwRnpLZnZxdllqNHNMOVlDSFZTVHNYMzlmZmJRWGgyZlM3X1lJS1VlT1MxT0hMVmNCM3hqZ2J5UlhxeHZNU1dqb1F2Mk1Ea0FpYVdVOFJxUnVzZXh5eWpsR25OUExwQUJ5Tklab1E5ZnAta0sxaGd3bklYajJveDA2SjVJWUY4ZklsSk9PMDJsQjhBRmtzZw?oc=5" target="_blank">Deep learning model using ECGs during sleep studies can predict cardiovascular outcomes</a>  <font color="#6f6f6f">National Institutes of Health (NIH) | (.gov)</font>

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Search Results for "Deep learning model using ECGs"

Updated every 3 minutes via FreeNewsApi, GNews, Currents API & Google News

29 stories displayed
Pairing cardiac magnetic resonance with electrocardiograms (ECGs) improved artificial intelligence (AI) assessments of cardiovascular diseases
2 Minute Medicinetech

Pairing cardiac magnetic resonance with electrocardiograms (ECGs) improved artificial intelligence (AI) assessments of cardiovascular diseases

<a href="https://news.google.com/rss/articles/CBMi_wFBVV95cUxORTJ1TXBmSTlqWnR2Z3NyNHBzeTBoWmU5ZUFRdUQ2UHpMZFpJblZ0QXphb1llR1RxZWxZcm5nbW5lSzE2Tnl0VFJINW9RYWlmYThVTVdaYjRSY19UQ1VBc3BNRm5kWU8wem1kcUczRVVPRmpoclhRLThiYk9FSHNXSVhSS3FmbllRM19hTkhrenVBSURuclVJMXF1bVd1Nm5mUWhDUHppRzdhR243X09Ycmo1RTY4enV0d2dzVzBudmlYQllUNFN6MjZEYlpjZ3BHaHJOMWpjcmZUZ3VKNk5TYWF2WklId082a2k4R3FZUUxJaVdlVVd4MVRZN0htUVU?oc=5" target="_blank">Pairing cardiac magnetic resonance with electrocardiograms (ECGs) improved artificial intelligence (AI) assessments of cardiovascular diseases</a>  <font color="#6f6f6f">2 Minute Medicine</font>

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An ECG biomarker for sudden cardiac death discovered with deep learning
Naturegeneral

An ECG biomarker for sudden cardiac death discovered with deep learning

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1jUnhZZUROcVFMbmRGeFRnYm9MVzgza3d1S0NfTXdJdmVqR0gtVXJLM1ROYWE0ekV2RUg2ck40U0FLLWNvZTNmeVB4N0FLX09WZTlfeFhyYU5UY2ZNdnZR?oc=5" target="_blank">An ECG biomarker for sudden cardiac death discovered with deep learning</a>  <font color="#6f6f6f">Nature</font>

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From one to twelve: feasibility and clinical utility of deep learning-derived 12-lead ECGs for remote cardiac monitoring
Frontiersgeneral

From one to twelve: feasibility and clinical utility of deep learning-derived 12-lead ECGs for remote cardiac monitoring

<a href="https://news.google.com/rss/articles/CBMiogFBVV95cUxPbDAzb1B6cWd5bVpPeXVBZGRFLVA5RF83N2JYc3M4VXdIYmc2MUNDNHZKMG1jaTc4SndfOWhqRFo5Y3dEdmt4Q2xLcjlnckxCdnYxeFJ4Skl5SVFhMWxaVnhzN1lFdXVsRjl3QjdaclFtVXJlaWF3bWFaWmtzMXA0S2Q5Unhlc2U3QlgyTEZmNFUwZG41ZFhab2E2VGo3SXFHa0E?oc=5" target="_blank">From one to twelve: feasibility and clinical utility of deep learning-derived 12-lead ECGs for remote cardiac monitoring</a>  <font color="#6f6f6f">Frontiers</font>

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Artificial Intelligence ECG Model Identifies Patients at Higher Risk for Sudden Cardiac Death
Patient Care Onlinegeneral

Artificial Intelligence ECG Model Identifies Patients at Higher Risk for Sudden Cardiac Death

<a href="https://news.google.com/rss/articles/CBMizAFBVV95cUxQU1o1TFh0dV96a1Bxa0NDS0JlaFViZGVBcWVrb1hQb05DMFpYYVR1aTJ1YXA4MDhrenplMEtrVUxHcUlJMFVmQm1IVHlFVkRoMnQ4WW95cFpVQmhwSlVIOWFYTWd5UlpvZ1VhNGlnRWtnRTlhMGowYklOTVlrQkphdF9fSmNEc3N5SFVUOE5KbjhpNmNkWVVGOGVmTERDTUpEUGgxem92LS1BMnlqWDh3THVMZEcyclN4SWFuVF9PMVVkTkxLWHB5LUw0U3E?oc=5" target="_blank">Artificial Intelligence ECG Model Identifies Patients at Higher Risk for Sudden Cardiac Death</a>  <font color="#6f6f6f">Patient Care Online</font>

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New AI algorithm could improve detection and prediction of heart diseases
Scripps Researchtech

New AI algorithm could improve detection and prediction of heart diseases

<a href="https://news.google.com/rss/articles/CBMitgFBVV95cUxPTWVSazh1QUtKQm9kenEtVG5zbDU1RFY4Rm5RcFZocXhReWJYXzI0b2JmUm1vYkVLdnNBSkVkVG5JdFlHLUlpNW9LWEFOYmNPQ0xnTHVlZjN4NDBJSGlHbENQckJYM0d4QjZubXd4bXF5dnhEMVZlcjF2M05KSDdsbnlxZjRSakZ5RjUwXzhzbTdrU0NKVVB5Q1k1a0lWQXdkeTF2S1lEWWR0TTJkMHA2TXo0VzRmZw?oc=5" target="_blank">New AI algorithm could improve detection and prediction of heart diseases</a>  <font color="#6f6f6f">Scripps Research</font>

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A hybrid deep learning algorithm for ECG-based heart disease classification
Naturegeneral

A hybrid deep learning algorithm for ECG-based heart disease classification

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9iOWR5ZEZnNDRBMUMzUnVia0pXNWplMW9qMEhFaHdMNXJEUkl1UUlKaXNyS2RxaDdET25OMU9sMncxTkMxbm1BcWhsRW1ybjF3akxWQ0lSdXo0dVFIbEhR?oc=5" target="_blank">A hybrid deep learning algorithm for ECG-based heart disease classification</a>  <font color="#6f6f6f">Nature</font>

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Higher-dimensional embedding of time-series data for machine learning
Frontiersgeneral

Higher-dimensional embedding of time-series data for machine learning

<a href="https://news.google.com/rss/articles/CBMiogFBVV95cUxQc1RfNnhXWk9tWEJqeFJKNGJMVGxsSW04RVV1aDduSE5FeUdkSk9kTjZDTjIwZHlYMWY4dTBxZk9fbGs1N05Ccy13QWp1VXVDZzBLZzlPb3MtWTFNVlhMWU9fVkRhZjh5eWx5MEx5b3Y4MXlpNE1taXlNdVRqSTZzMF9UcVdvTGNya0xpOTJ1azJFUnhVYU5STVBqaU0wLThKOFE?oc=5" target="_blank">Higher-dimensional embedding of time-series data for machine learning</a>  <font color="#6f6f6f">Frontiers</font>

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Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence
Naturetech

Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5SX3ZmOEtUcG5PeWh6a3pQbXp0akF0TUNRNDBpdmFRRmJfWWJhbDFJNjA1NGpudTNUR2k3U0Itc3dad3A1cWlFWTBvVEdfdHNsTzhRMGpkazI2ZmtoSms0?oc=5" target="_blank">Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence</a>  <font color="#6f6f6f">Nature</font>

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A deep learning ECG model for identification and localization of occlusion myocardial infarction
Naturegeneral

A deep learning ECG model for identification and localization of occlusion myocardial infarction

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBYX3MzS3pIeXdyQWtyNzNSS2d1elFiaWZHN3Q3eTFyZTFSUHU5aXE4VkRaUWFPbGRQTGh1eHBUTVdsTzNVVlpUelZYSHJtQmdGNVpCc2lNa0dBcHMxTjRF?oc=5" target="_blank">A deep learning ECG model for identification and localization of occlusion myocardial infarction</a>  <font color="#6f6f6f">Nature</font>

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CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification
Naturegeneral

CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5XS0RhODhHcEVtOGFoN0V1VEltZFNHNTVYRHE1TlVqQl9vbDA5NjdqYndLZVJpVVVOMm54SWtwT2J3eFhYS0RCNi0wbk1zX1F4VHBMN1BrV0tkSEY5Y0Jv?oc=5" target="_blank">CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification</a>  <font color="#6f6f6f">Nature</font>

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Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models
Naturetech

Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBGbGNOM1l4TENrZ2ZORzlkb004cGxXTTNhcG0zYktKcl9zVFR2Y0h1QVRBaFpVWTdqYXhpRGxNZUpEQ29WSzNGelR6VVo3OTlYU1hTWmdaVTV3cWxUZUFv?oc=5" target="_blank">Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models</a>  <font color="#6f6f6f">Nature</font>

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Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling
Naturetech

Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBaOUZWUzhNV29QejBBVEZOMjlEVDR2SmNyRFNIYnZjWXZWTURWMEExVTNhbFFWdmpLVnNucmJ4dHRPRE1NNVQ3WHYyN3ZscWV5T2xFRGpZb0lZbHppaGw4?oc=5" target="_blank">Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling</a>  <font color="#6f6f6f">Nature</font>

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Modeling day-long ECG signals to predict heart failure risk with explainable AI
Naturetech

Modeling day-long ECG signals to predict heart failure risk with explainable AI

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9HNU1yQmlZdXZWZWdHVndZdkdCWkpHeldLLXgzZk93ekhobjRpdlFyZEVVLUJkdVl1X05Qb05vYmdMd2ZsOTg1cE5vQkRkQlZLQWhaNm9XOGRpQ0tGQ1E4?oc=5" target="_blank">Modeling day-long ECG signals to predict heart failure risk with explainable AI</a>  <font color="#6f6f6f">Nature</font>

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MSCA-TNet based deep learning method for ECG arrhythmia classification
Naturegeneral

MSCA-TNet based deep learning method for ECG arrhythmia classification

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5VSUs1c05KcG12QXJvckVrWlozM0ZWRmVJRTc1R3BYNGV1N0RJTnpNS1RWbk1EVU1WR2U2dDRsbUo0MWlPWXdQVGpUMWRMU0k5SE1wbk5BaExreDFJYmtV?oc=5" target="_blank">MSCA-TNet based deep learning method for ECG arrhythmia classification</a>  <font color="#6f6f6f">Nature</font>

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Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification
Naturegeneral

Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFAxX25HWjFLelZTa2l1VGpxUHhaM042dEJ2d01BbnlPR3RGUGdYM1NNTkZfMElPRlFJWDJmanA2TDFhMkRhNjROdmpVdkU0VW1BSzg3N0JOSlZsNEVxRzFV?oc=5" target="_blank">Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification</a>  <font color="#6f6f6f">Nature</font>

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A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet
Naturegeneral

A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1BdktVaU1aa0h1S3Nja19weFhaYTdSTVdGbUtUZHAyUXQtQVB0Z2pDUTJqNVpyU3M5c05Pekw2YXR4Nno5Zl9MamtEZXdLUGdzcW90X2RHY3VPcW9qXzhJ?oc=5" target="_blank">A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet</a>  <font color="#6f6f6f">Nature</font>

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An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection
Naturegeneral

An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBGSGg4RkFTZ01fcl8xS2dSOXdUdEVEWThFRzROS0Q3RnNIU3RZUnQwTDg5MnFMV1FIbFBkNURSWm9KS1Y3dTUzbHNLMklxNUNMYjNYamJZXzdaZ2N0VUE4?oc=5" target="_blank">An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection</a>  <font color="#6f6f6f">Nature</font>

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CODE-II: a large-scale dataset for artificial intelligence in ECG analysis
Naturegeneral

CODE-II: a large-scale dataset for artificial intelligence in ECG analysis

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9KT01ka182aFg0UnFGQkRla0dabE9XZE9jbmNMZVVTNzZtRDJKN1hYTmNIcEJYU2k1ZDZBb0F5WDJTWkFPajJrNXAyRWNoNHBzRjEtZzZIa0hVeXpobHcw?oc=5" target="_blank">CODE-II: a large-scale dataset for artificial intelligence in ECG analysis</a>  <font color="#6f6f6f">Nature</font>

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Time series electrocardiography (ECG) data for early prediction of cardiac arrest
Naturegeneral

Time series electrocardiography (ECG) data for early prediction of cardiac arrest

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE04WVV0bW1GNUxxTmgxbHBxT3BXMzEtaXd5WHowY2NyWnNoSHVrbk5fQ2ZLZlg3TnQ5aHlZSEdrcGJtWk1tWTJYMFdwTXlJanRuSVBsUWVXNFIwTV9PT0FB?oc=5" target="_blank">Time series electrocardiography (ECG) data for early prediction of cardiac arrest</a>  <font color="#6f6f6f">Nature</font>

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Machine learning–enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support
Naturegeneral

Machine learning–enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE0xTFZEVTNfdnlFY2hmWGdLM2d2SzY5NWFDTXdMajF1Rk5UdW1iYXFhNGxWYk9xOTZ0cDRyd3J3UVBFTlVMODVTdGp0OGh5ZTZSMUgzanBjNUtGaV9DTllR?oc=5" target="_blank">Machine learning–enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support</a>  <font color="#6f6f6f">Nature</font>

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AI-enhanced approaches for personalized cardiac treatment: insights from ECG data
Naturetech

AI-enhanced approaches for personalized cardiac treatment: insights from ECG data

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE90S1ZyOC0wd3BSUjU3VGVJSnQxRUR4d1ZHQ2EzWXotTmp6TThrNS1BWEdnWHBoVjNuN0VKZE9LaTNRWUwta2duME9OWnlVbnFoRVl1MndRT1pKTElYY3h3?oc=5" target="_blank">AI-enhanced approaches for personalized cardiac treatment: insights from ECG data</a>  <font color="#6f6f6f">Nature</font>

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Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram
medRxivgeneral

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

<a href="https://news.google.com/rss/articles/CBMifEFVX3lxTE0wRmVrTm96VnJJaHc2a0lab2h6ZjhDY05KSnpYT05DVFBPc3FPUUxONmNEWnhjbVMwaTdpa0hwSkMtX1ZuQ0FIdW83TjRGelFjM0JLaFVKbHdRYy1rZ0xJbjN5NWZqYTFEUDhaMVBkcVNEandJVWNFTmNKWlo?oc=5" target="_blank">Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram</a>  <font color="#6f6f6f">medRxiv</font>

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Risk stratification of patients with syncope in the emergency department using ECG based artificial intelligence models
Naturegeneral

Risk stratification of patients with syncope in the emergency department using ECG based artificial intelligence models

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9abVdhYjBMLVlEVHdwVjNLb3Rxc3JCam9xSTlBOWkwOW5OQlBuMkF6TzM3UTZLU0JsMnR5V3RFVzRkS182WHpzamdkejFsSHJfQVk4Ykt2NkY2MjhzNnZB?oc=5" target="_blank">Risk stratification of patients with syncope in the emergency department using ECG based artificial intelligence models</a>  <font color="#6f6f6f">Nature</font>

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Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis
medRxivgeneral

Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis

<a href="https://news.google.com/rss/articles/CBMifEFVX3lxTE05S2Ezbk9xWGpzSUM5VmpTVUpEczhKSjk3TlUtcE16Q3d1dF9PbWVCbUdhd1BuN01YRVJod083aU5STnVndi10NUZBSUQ5R2pVdGNkTmpKYlFGSGhWWWlEY2VKUnhSdmd0U25VRDUxTTNjZ0xzbkNMdFVReVU?oc=5" target="_blank">Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis</a>  <font color="#6f6f6f">medRxiv</font>

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Artificial Intelligence-Enabled Electrocardiography for Potassium Abnormality Detection and Estimation: A Systematic Review
Cureusgeneral

Artificial Intelligence-Enabled Electrocardiography for Potassium Abnormality Detection and Estimation: A Systematic Review

<a href="https://news.google.com/rss/articles/CBMi8wFBVV95cUxQeUVnQVFkSjJ4cHpjZ0l2MUdyalk1akx0Vy1PQkM0SThHUjJ6VFp0RkI2UVdCdmlzWUxWcUdpOGszeTN4TW1NUWx3U0RwNkVGZExRRkRNaEFfZUpGYTRvUlRNQUx1dy05cXlBZUd5S01TQVA1MkQyRFhlLTYzOUVsQ2FOQzFmd1RXOXdPLUEzRjlSN0FqX20ta2FOcjhmZ0t1X2V2Vnp4bFREM0F1VU5xQnlVS2o1YXBidEdhREpqQ05VX2kwdWl5cU5jdTZ5SnJrLUR6WUlDSWt6X0p3aFV1TFg5WmdSVEZMdmdWNjB3c09SbHM?oc=5" target="_blank">Artificial Intelligence-Enabled Electrocardiography for Potassium Abnormality Detection and Estimation: A Systematic Review</a>  <font color="#6f6f6f">Cureus</font>

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Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction
Cureusgeneral

Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction

<a href="https://news.google.com/rss/articles/CBMivwFBVV95cUxQZ0Z1Q0RkQUNDeU1tOEdOaVpDM3IzbXE0Vjl4Q3pfS1dxSmpTQVlKX1J5RmdTVXVBb2I0OGZ0MTdQeEdSWjFUcWhfUzlJd0E0VWltdXBtcWNOZWdUYlZLcUN1NVJDeENFZkZpQmFYQ0N6aHNkLVhtSV9hRU56MXNCMkNuWHZvbGhGblo5VzB3SU92WWYtZ29NRk12X19pb0hZN3RCZ2I4cFNYVG03S1dCVlRaVVN2X1BaeXFzZU5SOA?oc=5" target="_blank">Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction</a>  <font color="#6f6f6f">Cureus</font>

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Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning
Naturegeneral

Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1zSkRPSldsUzN6bnNJekR0NlNac3NXVlR3LWJfUEdILWFhME0xVzBzU2FCdGNXb1dlVV9vTWdpQldiblhzOHU2STc1WVYtMmstTkJXeFM3SWZXbFhwVXhr?oc=5" target="_blank">Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning</a>  <font color="#6f6f6f">Nature</font>

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Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank
medRxivmarkets

Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank

<a href="https://news.google.com/rss/articles/CBMic0FVX3lxTFBpZ2lVSzdfdjdlYllmc1B5STVSNjVRWlkxcnVPVU4xejN3M3lFRTZ0NmdDU0RqWEtHRW1sNXF3YThVbUxyWU1PRkxIQkdfNEFNc1ZUcWZScnFKckNZeTNmRHZjQVhEdmlyd1I3N0xMSWNhV1E?oc=5" target="_blank">Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank</a>  <font color="#6f6f6f">medRxiv</font>

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Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach
Naturegeneral

Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFAxWUR6eWxDMlh1dnhFbG5pZElOaTRqaDdXM0hZNEtKTTdaa19kNXFMYVl5dzNydlFCRXZXclpnRjZuelJDWWFnSzBsUURfNnRWNUxfTENYV3pxRkJsTGhF?oc=5" target="_blank">Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach</a>  <font color="#6f6f6f">Nature</font>

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"Deep learning model using ECGs" — Live Google News Trends & Headlines