Adaptive Blind Elimination of Artifacts in ECG Signals

Allan Kardec Barrosl, Ali Mansour2 and Noboru Ohnishi 1,2
1 - Graduate School of Engineering
Department of Information Engineering, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya 464-01, Japan.
2 - RIKEN BMC Research Center, Nagoya, Japan.

    abstract- In this work, we deal with the elimination of artifacts
     (electrodes, muscle, respiration, ete-) from the electrocardiographic
     (ECG) signal. We use a new tool called independent component analysis
     (ICA) that blindly separate mixed statistical independent signals.
     ICA can separate the interference, even if they overlap in frequency.
In order to estimate the mixing parameters in real-time, we propose a
self-adaptive step-size, derived from the study of the averaged behavior
 of those parameters, and a two- layers neural network. Simulations were
  carried out to show the performance of the algorithm using a standard ECG database.

Keywords: Independent component analysis, blind separation, adaptive filtering cardiac artifacts, ECG analysis.