Standard ECG lead I prospective estimation study from far-field bipolar leads on the left upper arm: A neural network approach. (May 2019)

• Record Type: Journal Article
• Authors: Vizcaya, Pedro R.; Perpiñan, Gilberto I.; McEneaney, David J.; Escalona, Omar J.
• Is Part Of: Biomedical signal processing and control. Volume 51(2019)
• Page Start: 171
• Page End: 180
• Publication Date: 2019-05
• Subjects: Electrocardiography -- Biomedical monitoring -- Wearable sensors -- Biological information theory -- Mutual information -- Artificial neural network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.01.020
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: In this study, the feasibility of interpreting heart rhythms from far-field bipolar ECG arm-band lead recordings on the left-upper-arm (LUA), is evaluated in a clinical multichannel arm-ECG mapping database (N = 153 subjects) for the prospective development of long-term heart rhythm monitoring from comfortable arm wearable devices. A preliminary multivariable linear regression analysis on ECG chest Lead I from 10 selected far-field bipolar leads along the left arm, indicated that 3 of them in the LUA were relevant and worth evaluating in more detail from a heart rhythm information perspective. To derive a good and effective estimation process, a time series non-linear regression point estimator, using an artificial neural network with 2 lags was investigated, showing a correlation coefficient of up to 0.969 for a single subject. Then, a vector approach was adopted for the whole LUA database, aiming to develop a subject independent estimation process of the P-QRS-T waveform interval and its heart rhythm attributes in the standard chest Lead I. In the same study, the first 96 coefficients, of the Discrete Cosine Transform on the P-QRS-T interval were used as a means for reducing the dimensionality of the input space, with a loss of just 0.1% in power, and reducing the dimensionality to just 5% of the original size. The trained ANN for ECG Lead I estimation from one upper arm Lead-1 showed a correlation coefficient above 80% on a beat-to-beat basis, an improvement on all but 1.34% of the beats estimated for a typical train/test partition of the LUA database. The non-triviality of the results was tested with random and intentional true negatives. Information theory analytics revealed that there is an estimated information of 1.6 bits/beat between LUA armband bipolar leads and the standard Lead I.