Accurate and reliable 3-lead to 12-lead ECG reconstruction methodology for remote health monitoring applications

Abstract

Standard 12-lead (S12) system and Mason-Likar 12-lead (ML12) system despite of being most acceptable systems for clinical usage are not the preferred lead systems for remote monitoring (RM) applications. Usually RM applications involve wireless transmission of signals and a 2-3 lead system is preferred for bandwidth and storage limitations and data transmission time. Generally, ECG compression techniques are applied for the same, however, compression ratio (CR) depends on the number of channels and decreases with the increase in number of channels. Thus, it facilitates the usage of a 2-3 lead system. However, a reduced lead (RL) system with 2-3 leads may be inadequate for the information desired by the cardiologists who are accustomed to S12 or ML12 system pertaining to its decades old usage. In this paper, we attempt to provide solution to both technical and non-technical limitations of RM applications. We reconstruct S12 and ML12 systems from Reduced 3-lead (R3L) system comprising of basis leads I, II, V2 using personalized or patient-specific transformation. Two separate investigations have been carried out for S12 and ML12 with their corresponding R3L systems comprising of their respective basis leads. PhysioNet PTBDB and INCARTDB after wavelet based preprocessing were used in this investigation. R 2 statistics, correlation (rx) and regression (bx) coefficients were used to evaluate reconstructed signal against the original signal and the mean values obtained were 96.53%, 0.982 and 0.968 (S12) and 96.53%, 0.982 and 0.968 (ML12) respectively. R3L system reduces number of leads and electrodes from 12 and 10 to 3 and 5 respectively, lowers bandwidth and storage requirements, data transmission time and increases CR. The study shows that basis leads obtained from S12 outperforms the basis leads of ML12 for reconstruction of precordial leads.

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

“…Although many methods have been reported, their direct comparison is questionable due to their differences in: a) types of heartbeats being classified (normal vs ischemic 3 – 6 , normal vs ventricular premature beats – VPBs 7 – 14 etc. ); b) ECG features (morphological 4 , 5 , 7 , 15 , 16 spectral 8 , 14 first-order or higher order statistics 10 , 12 non-linear 9 , heart-vector projection 17 etc. ); c) classification models (discriminant function 4 , 9 cluster analysis 14 , artificial neural network 18 , naive Bayes classifier 13 , support vector machine 6 , 7 k-nearest neighbors 5 , 11 etc.).…”

Personalized Reduced 3-Lead System formation methodology for Remote Health Monitoring Applications and Reconstruction of Standard 12-Lead system

“…Lead Component (LC) system, formation methodology targeting the emerging field of remote health monitoring to improve diagnosis and prognosis by overcoming the constraints from technical and medical science side. It has been shown that the proposed LC system out-performs the existing methodologies [10,12,15,20,21] by various investigators for the reconstruction of standard 12-leads and has been verified on two widely accepted databases viz. PTBDB and TWADB of PhysioNet.…”

“…None of the aforementioned methodologies aimed at finding application in remote health monitoring. In our previous works [11,15,21,22], we have focused on this applications of lead reconstruction in a RHM scenario. All possible combinations of subsets of 3-leads of S12 system (S12S systems) have been used to reconstruct the missing leads using personalized methodology and have been shown to produce superior results compared to other investigations by various researchers.…”

“…The preprocessing module comprises of baseline wandering (BW) removal based on discrete wavelet transform (DWT) [31] and denoising based on trans-lation invariant wavelet transform (TIWT) [32]. The snippet of the MATLAB code for implementation of the preprocessing module and its methodology can be found in [21].…”

“…The results obtained for the original and reconstructed signal from the TDMG algorithm were then compared using RMSE. [10,21,29] and RMSE have been used as performance evaluation metrics. All signals were mean centered before applying these metrics.…”