Open AccessA Novel 2-Lead to 12 Lead ECG Reconstruction Methodology for Remote Health Monitoring Applications
Author(s) -
Naresh Vemishetty,
Vishnuvardhan Gundlapalle,
Amit Acharyya,
Bhudeb Chakravarti
Publication year - 2020
Publication title -
2019 computing in cardiology (cinc)
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.257
H-Index - 55
ISSN - 2325-887X
ISBN - 978-1-7281-6936-1
DOI - 10.22489/cinc.2019.231
Subject(s) - bioengineering , computing and processing , signal processing and analysis
Standard clinical 12-lead ECG is the setup most commonly used by the doctors for the reliable diagnosis. However, placing all the electrodes on the body will be inconvenient and affect the daily activities of the patient. In addition, transmitting all the lead information will require high power and large memory in remote ECG monitoring. Accounting this, we propose here a novel 2 to 12 lead reduction system for remote ECG monitoring applications. In the proposed method, the coefficient values of all the leads are generated as the first step using the least-square fit method and heart-vector projection. For the 12 lead ECG reconstruction, lead I and V2 are taken as the basis leads. As a part of the reconstruction, Lead II is derived initially from these two basis leads (Lead I, V2) using the HVP computation. After LeadII derivation, these three (I, II, V2) leads acts as basis leads and will derive remaining leads (V1, V3, V4, V5, V6) by repeating the HVP computation. Three categories Healthy Control (HC), Bundle Branch Block (BBB) and, Myocardial Infarction (MI) were taken from PTBDB for the proposed work. R 2 statistics, correlation and regression coefficients were used to evaluate the performance, the mean values of the stated performance metrics obtained were 91.94%, 0. 957, 0.921 for HC, 85.81%, 0.920, 0.856 for BBB and 81.42%, 0.889, 0.820 for MI respectively.