Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture

Extracting the Electrocardiogram (ECG) of a fetus from the ECG signal of the maternal abdomen is a challenging task due to different artifacts. The paper proposes a
$N$
-tap non-causal adaptive filter (NC-AF) that update the weight by considering the
$N$
number of past weights and
$N-1$
number of the reference signal and error signal samples after the processing sample number
$n$
. Using the maternal abdominal signal as the primary signal and thorax signal as the reference input, the output
$e(n)$
is obtained from the mean of
$N$
number of errors. The filtering performance of NC-AF was evaluated using the Synthetic dataset and Daisy dataset with the metrics such as correlation coefficient (
$\gamma$
), peak root mean square difference (PRD), the output signal to noise ratio (SNR), root mean square error (RMSE), and fetal R-peak detection accuracy (FRPDA). The NC-AF provides a maximum correlation coefficient, PRD, SNR, RMSE and FRPDA of 0.9851, 83.04%, 8.52 dB, 0.208 and 97.09% respectively with filter length
$N=38$
. The paper also proposes the architecture of NC-AF that can be implemented in hardware like FPGA. Further, the NC-AF was implemented on Virtex-7 FPGA and its performance is evaluated in terms of resource utilization, throughput, and power consumption. For filter length
$N=38$
and wordlength
$L=24$
, the maximum performance of the filter can be attained with a power consumption of
$\text{1.287} W$
and a maximum clock frequency of 139.47 MHz.