Reconstruction of Precordial Lead Electrocardiogram From Limb Leads Using the State-Space Model.

Abstract

A new electrocardiogram (ECG) reconstruction method based on a state-space model is presented. This method was applied to reconstruct precordial leads from limb leads (lead I, II, III) for its validity verification. The system matrices of the state-space model were estimated at the model estimation stage by considering the limb lead signals as the input of the system and precordial lead signals as the output. To evaluate the performance of the proposed method, all of the 549 records of the Physikalisch Technische Bundesanstalt diagnostic ECG database were used, and the correlation coefficients (CC) and root-mean-square errors between reconstructed ECG and measured ECG were calculated. For a more objective evaluation, the results were compared with those of linear regression model that has been typically used for ECG reconstruction. The mean and median values of CCs were higher than 0.988 and 0.995, respectively, for healthy subject data, and also higher than 0.981 and 0.993, respectively, for cardiac patient data and comparable to those by linear regression model. In addition, it was found that the reconstruction performance depended on the type of disease rather than lead type. Among cardiac patient data, hypertrophy, myocarditis, valvular heart disease, and stable heart angina showed higher CC (>0.990), while unstable angina and heart failure showed lower CC of 0.932 and 0.914, respectively. Moreover, when ECG contaminated with the noise was used for reconstruction, the proposed method demonstrated better performance than linear regression model in general.