Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA

Abstract

In this Letter, the field programmable gate array (FPGA) implementation of a foetal heart rate (FHR) monitoring system is presented. The system comprises a preprocessing unit to remove various types of noise, followed by a foetal electrocardiogram (FECG) extraction unit and an FHR detection unit. To improve the precision and accuracy of the arithmetic operations, a floating-point unit is developed. A least mean squares algorithm-based adaptive filter (LMS-AF) is used for FECG extraction. Two different architectures, namely series and parallel, are proposed for the LMS-AF, with the series architecture targeting lower utilisation of hardware resources, and the parallel architecture enabling less convergence time and lower power consumption. The results show that it effectively detects the R peaks in the extracted FECG with a sensitivity of 95.74–100% and a specificity of 100%. The parallel architecture shows up to an 85.88% reduction in the convergence time for non-invasive FECG databases while the series architecture shows a 27.41% reduction in the number of flip flops used when compared with the existing FPGA implementations of various FECG extraction methods. It also shows an increase of 2–7.51% in accuracy when compared to previous works.

Keywords

• electrocardiography
• least mean squares methods
• medical signal processing
• adaptive filters
• flip-flops
• field programmable gate arrays
• obstetrics
• series architecture
• existing fpga implementations
• fecg extraction methods
• lms adaptive filter-based fecg extraction
• fpga implementation
• foetal heart rate monitoring system
• preprocessing unit
• foetal electrocardiogram extraction unit
• fhr detection unit
• arithmetic operations
• floating-point unit
• mean squares
• lms-af
• lower utilisation
• parallel architecture
• convergence time
• extracted fecg
• noninvasive fecg databases