Optimal design of lattice wave digital fractional‐order Butterworth filter

2020 ◽  
Author(s):  
Shibendu Mahata ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Optimal Design ◽  
Fractional Order ◽  
Butterworth Filter ◽  
Lattice Wave ◽  
Order Butterworth Filter

Optimal design of fractional order low pass Butterworth filter with accurate magnitude response

2018 ◽  
Vol 72 ◽  
pp. 96-114 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Optimal Design ◽  
Fractional Order ◽  
Magnitude Response ◽  
Butterworth Filter ◽  
Low Pass

scholarly journals Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction

2021 ◽  
Vol 12 (05) ◽  
pp. 45-56
Author(s):  
Hadi Mohsen Alkanfery ◽  
Ibrahim Mustafa Mehedi
Keyword(s):  
Fractional Order ◽  
Reference Signal ◽  
Model Performance ◽  
Correlation Technique ◽  
Noise Sources ◽  
Butterworth Filter ◽  
Fetal Ecg ◽  
Fast Independent Component Analysis ◽  
Order Butterworth Filter ◽  
Cross Correlation Technique

The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.

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