Standard ECG Lead I Prospective Estimation Study from Far-field Bipolar Leads on the Left Upper Arm: A Neural Network Approach

Pedro Vizcaya, Gilberto Perpiñan, David McEneaney, OJ Escalona

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8 Citations (Scopus)
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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.
Original languageEnglish
Article numberBSPC 1471
Pages (from-to)171-180
Number of pages10
JournalBiomedical Signal Processing and Control
Early online date4 Mar 2019
Publication statusPublished (in print/issue) - 31 May 2019


  • Artificial neural network
  • Biological information theory
  • Biomedical monitoring
  • Electrocardiography
  • Mutual information
  • Wearable sensors


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