Abstract
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%.
Highlights
Facts mentioned that around 2.6 million people above 15 years old in Indonesia suffered from coronary heart disease [1]
Optimal electrodes location for each segment is different, as investigated by Finlat et al [7].They introduced Eigenleads which is useful for pre-diagnosing heart pathologies and for wearable ECG, which requires high signal to noise ratio (SNR)
We propose a new approach by utilizing different EASI coefficients for each ECG segment; it is segment-specific (SS)
Summary
Facts mentioned that around 2.6 million people above 15 years old in Indonesia suffered from coronary heart disease [1]. The standard 12-lead ECG with ten electrodes has been established as diagnostic reference in hospitals It is impractical for 24-hours monitoring, wearable, and ambulatory applications due to difficulty to attach electrodes and sensitivity to wiring noise and motion artifacts [9]. To improve EASI coefficients, several techniques have been presented such as Dower’s method, Linear Regression (LR), Polynomial Regression (PR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) [9]. In those techniques, the coefficients were calculated by utiizing full cycle (FC) of ECG signal, i.e. all segments. The proposed approach, i.e. SS, was compared with the conventional one, i.e. FC
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