Real-time fetal ECG system design using embedded microprocessors

Abstract

Monitoring the fetal ECG (FECG) gives us important information about the fetal wellbeing. FECG is a complex waveform where each of the P through T complexes provides wealth of information. The objective of this project is to develop the best state-of-art real-time FECG monitoring system using embedded microprocessors. Many researchers from various fields like signal processing, artificial intelligence, and advanced statistics, have applied different techniques to extract FECG from the mixture of MECG and other noises and calculate the FHR, with accuracy as an objective. Most of them are calculation intensive and not real-time. The proposed approach focuses mainly on real time processing, robustness and portability of the system. The work discussed here will provide a novel algorithm to extract FECG from abdominal ECG (AECG) which is mixture of FECG, MECG, and noise, and finding Fetal FHR with less number of dimensions (measurements) with the best signal-to-noise ratio. This approach is tested on different soft-core processors and results are compared with other commercial of-the-shelf (COTS) hardcore solutions, in terms of power, cost, size and speed. In the end FECG was successfully extracted and identified on the basis of BPM and SNR values calculated using this method. It was found that hard-core processor (ARM Cortex A9) has achieved the best real-time performance among all.

PCA-Based Preprocessing for Clustering-Based Fetal Heart Rate Extraction in Non-Invasive Fetal Electrocardiograms

“…The principal components capture the power of most of the signal and, at the same time, all components are orthogonal to each other. That is why PCA sometimes is also called a whitening operation [15,40,49]. Thus, the PCA method fits perfectly with one of the main objectives of this work, which is the generation of a single aECG master signal without discarding any of the original channels from the aECG recording, in order to subsequently apply techniques for the extraction of fetal information of interest such as the fHR.…”

Efficient Analysis of Large-Size Bio-Signals Based on Orthogonal Generalized Laguerre Moments of Fractional Orders and Schwarz–Rutishauser Algorithm

“…The complexity of the Schwarz-Rutishauser algorithm is (mn 2 ), which is (n)-times less than the complexity of the classical QR decomposition methods: GSM, HM, and GRM, which motivated us to use it in the proposed algorithm, which obtains better results concerning complexity and numerical stability. Uwe et al [32] proved the efficiency of the Schwarz-Rutishauser algorithm concerning real-time fetal ECG monitoring systems, and it helped in raising the efficiency of the proposed system in terms of improving the system performance and energy consumption rate. To confirm the robustness of the FrGLMs with Schwarz-Rutishauser, samples of ECG bio-medical signal are used.…”