Comput. Biol. Med. - Research of fetal ECG extraction using wavelet analysis and adaptive filtering.

Tópicos

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Resumo

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.

Resumo Limpo

extract clean fetal electrocardiogram ecg signal import fetal monitor paper propos new method fetal ecg extract base wavelet analysi least mean squar lms adapt filter algorithm spatial select nois filtrat ssnf algorithm first abdomin signal thorac signal process stationari wavelet transform swt wavelet coeffici scale obtain scale detail coeffici process lms algorithm coeffici abdomin signal taken origin input lms adapt filter system coeffici thorac signal refer input correl process wavelet coeffici comput threshold set nois compon remov ssnf algorithm final process wavelet coeffici reconstruct invers swt obtain fetal ecg twenti case simul data case clinic data use experiment result show propos method outperform lms algorithm show improv case superposit rpeak fetal ecg matern ecg nois disturb elimin incorpor ssnf algorithm extract waveform stabl perform proven quantit snr calcul result indic propos algorithm can use extract fetal ecg abdomin signal

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