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AI Cardiovascular language learning based

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TaraGonzales5

This document discusses using artificial intelligence to analyze electrocardiogram (ECG) time series data to detect cardiovascular anomalies. 315 ECG images were collected from a health camp and analyzed using an AI-enabled computational environment built with R programming language, MySQL, and Linux. The AI system used Earth Mover's Distance to compare the ECG images to a normal threshold image and detect anomalies like acute myocardial infarction, arrhythmias, and other conditions. The results demonstrated the potential for AI to accurately and precisely detect cardiovascular diseases from ECG data.

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Artificial Intelligence Enabled Visualization Cardiovascular Anomalies (Based Upon ECG Timeseries) Shazina Saeed1, Kumar Dron Shrivastav1, Priyadarshini Arambam 1,3, Mehak Segan1, Ankan Mukherjee Das1, Priya Ranjan2, Upendra Kaul 3 and Rajiv Janardhanan1 Amity Institute of Public Health 1Amity Institute of Public Health, 2Amity School of Engineering and Technology, Amity University, Noida, UP, 3 Department of Academics and Research, Batra Hospital and Medical Research Centre, New Delhi
Amity Institute of Public Health
Amity Institute of Public Health
Amity Institute of Public Health
AI Enabled Systems as Strategies for Affordable & Accessible Healthcare AI Based Health Systems Electronic health record e-Libraries of Genomics & Metabolomics Data Prescription Pharmacy Patient registration or scheduling Monitoring, evaluation of patient behaviour Clinical decision support Personalized Therapeutic Stratégies Telemedicine Research/data collection Amity Institute of Public Health
AI Enabled Analysis of Cardiovascular Health Camp Amity Institute of Public Health
AI enabled Automated Interpretation of ECG traces as a tool for Accurate and Precision Oriented Detection of CVD Anomalies Amity Institute of Public Health
'R' version 3.4.4 as programming language MySQL version 14.14 Distribution 5.5.60 for creation of image repository LINUX version 14.04 as base platform Sample Size – 315 ECG images of different individuals were acquired during a health camp in Sangam Vihar, New Delhi from Batra Hospital & Medical Research Centre, New Delhi AI Enabled Computational Environment Amity Institute of Public Health
Normal ECG Threshold Image Contoured image with background removed Amity Institute of Public Health
S. No. ECG category ECG category EMD 1 Acute anterolateral MI Normal 2.247734 2 Acute inferior MI Normal 5.114204 3 Acute posterior MI Normal 0.4006917 4 Acute right ventricular MI Normal 3.778381 5 Acute septal MI Normal 1.631825 6 Dual chamber pacemaker Normal 2.074434 7. Hyperkalemia Normal 1.839777 8. Hypokalemia Normal 1.915754 9. Pericarditis Normal 5.385918 10 Ventricular tachycardia Normal 7.210131 11. Supra ventricular tachycardia - AV reentry Normal 4.998083 12. Supra ventricular tachycardia - AV nodal Normal 0.5161946 EMD result Amity Institute of Public Health
EMD Based Representation of the Cardiac Anomalies ( Based Upon ECG Traces) Amity Institute of Public Health
Dr. Rajiv Janardhanan Dr. Priya Ranjan Dr. Shazina Saeed Mr. Kumar Dron Shrivastav Mr. Ankan Mukherjee Das Acknowledgments : The initial sets of ECG Images were acquired from Batra Hospital & Medical Research Centre, New Delhi and QUEEN'S University, School of Medicine web portal-https://meds.queensu.ca/central/assets/modules/ts- ecg/normal_ecg.html. Amity Institute of Public Health

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AI Cardiovascular language learning based

  • 1. Artificial Intelligence Enabled Visualization Cardiovascular Anomalies (Based Upon ECG Timeseries) Shazina Saeed1, Kumar Dron Shrivastav1, Priyadarshini Arambam 1,3, Mehak Segan1, Ankan Mukherjee Das1, Priya Ranjan2, Upendra Kaul 3 and Rajiv Janardhanan1 Amity Institute of Public Health 1Amity Institute of Public Health, 2Amity School of Engineering and Technology, Amity University, Noida, UP, 3 Department of Academics and Research, Batra Hospital and Medical Research Centre, New Delhi
  • 2. Amity Institute of Public Health
  • 3. Amity Institute of Public Health
  • 4. Amity Institute of Public Health
  • 5. AI Enabled Systems as Strategies for Affordable & Accessible Healthcare AI Based Health Systems Electronic health record e-Libraries of Genomics & Metabolomics Data Prescription Pharmacy Patient registration or scheduling Monitoring, evaluation of patient behaviour Clinical decision support Personalized Therapeutic Stratégies Telemedicine Research/data collection Amity Institute of Public Health
  • 6. AI Enabled Analysis of Cardiovascular Health Camp Amity Institute of Public Health
  • 7. AI enabled Automated Interpretation of ECG traces as a tool for Accurate and Precision Oriented Detection of CVD Anomalies Amity Institute of Public Health
  • 8. 'R' version 3.4.4 as programming language MySQL version 14.14 Distribution 5.5.60 for creation of image repository LINUX version 14.04 as base platform Sample Size – 315 ECG images of different individuals were acquired during a health camp in Sangam Vihar, New Delhi from Batra Hospital & Medical Research Centre, New Delhi AI Enabled Computational Environment Amity Institute of Public Health
  • 9. Normal ECG Threshold Image Contoured image with background removed Amity Institute of Public Health
  • 10. S. No. ECG category ECG category EMD 1 Acute anterolateral MI Normal 2.247734 2 Acute inferior MI Normal 5.114204 3 Acute posterior MI Normal 0.4006917 4 Acute right ventricular MI Normal 3.778381 5 Acute septal MI Normal 1.631825 6 Dual chamber pacemaker Normal 2.074434 7. Hyperkalemia Normal 1.839777 8. Hypokalemia Normal 1.915754 9. Pericarditis Normal 5.385918 10 Ventricular tachycardia Normal 7.210131 11. Supra ventricular tachycardia - AV reentry Normal 4.998083 12. Supra ventricular tachycardia - AV nodal Normal 0.5161946 EMD result Amity Institute of Public Health
  • 11. EMD Based Representation of the Cardiac Anomalies ( Based Upon ECG Traces) Amity Institute of Public Health
  • 12. Dr. Rajiv Janardhanan Dr. Priya Ranjan Dr. Shazina Saeed Mr. Kumar Dron Shrivastav Mr. Ankan Mukherjee Das Acknowledgments : The initial sets of ECG Images were acquired from Batra Hospital & Medical Research Centre, New Delhi and QUEEN'S University, School of Medicine web portal-https://meds.queensu.ca/central/assets/modules/ts- ecg/normal_ecg.html. Amity Institute of Public Health