Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique

Abstract

Abstract-This paper presents a method for fetal heart rate estimation from an abdominal electrocardiogram (ECG) signal based on adaptive filter analysis using least mean square (LMS) adaptive filtering algorithm in order to determine the health status of a baby in its mother's womb. The fetal ECG signal is extracted from abdominal ECG containing other sources of interference using the maternal ECG signal obtained from mother's chest cavity as the reference signal. Interference/noise model used for this work include the power-line noise, the white noise and the unwanted propagating maternal ECG signal. Thereafter, the heart rate is estimated using an automated peak voltage measurement algorithm at 75 percent threshold voltage. It is found that irrespective of the estimated heart rate of the baby, 100 percent estimation is achieved at signal-to-noise ratio (SNR) greater than or equal to -31dB.

The Feature Extraction to Determine the Wave’s Peaks in the Electrocardiogram Graphic Image

“…In [10], the pre-processing by using the grayscale and binary, while the feature extraction changes the time scale and voltage. While [11], [12], [13], [14], [15], [16], and [17] conduct some researches about ECG signal.…”

Estimation of Noise in Nonstationary Signals Using Derivative of NLMS Algorithm

“…The adaptive filter is still better tool to suppress the background noise and estimate the original signal in noisy environment for both speech and bioelectric signals [21]. Researchers are worked on different forms of adaptive filter like least mean square (LMS), Leaky LMS, NLMS and Recursive least square (RLS) algorithm for speech enhancement [25], removal of noise in EEG and extraction of fetal ECG from maternal ECG [20]. In the basic adaptive filter like least mean square (LMS) algorithm the step size remains constant in weight equation for all the input samples.…”