Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

Indra Hardian Mulyadi, Nelmiawati Nelmiawati, Eko Supriyanto


A number of methods have been proposed to reduce number of leads for electrocardiography (ECG) measurement without decreasing the signal quality. Some limited sets of leads that are nearly orthogonal, such as EASI, have been used to reconstruct the standard 12-lead ECG by various transformation techniques including linear, nonlinear, generic, and patient-specific. Those existing techniques, however, employed a full-cycle ECG waveform to calculate the transformation coefficients. Instead of calculating the transformation coefficients using a full-cycle waveform, we propose a new approach that segments the waveform into three segments: PR, QRS complex, and ST, hence the transformation coefficients were segment-specific. For testing, our new segment-specific approach was applied to six existing methods: Dower’s method with generic coefficients, Dower’s method with individual (patient-specific) coefficients, Linear Regression (LR), 2nd degree Polynomial Regression (PR), 3rd degree PR, and Artificial Neural Network (ANN). The results showed that the new approach outperformed the conventional full-cycle approach. It was able to significantly reduce the derivation error up to 74.50% as well as improve the correlation coefficient up to 0.66%.


Artificial neural network; Derived 12-lead; Electrocardiography; EASI; Personalized ECG; Regression;

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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