ECG QRS Complex detection with programmable hardware.

Area of Science:

• Biomedical Engineering
• Digital Signal Processing
• Hardware Acceleration

Background:

• Existing algorithms for ECG QRS Complex detection, while effective, suffer from high computational intensity.
• The need for efficient and accurate QRS detection is critical for both clinical diagnosis and wearable health monitoring devices.

Purpose of the Study:

• To propose a novel algorithm and hardware architecture for ECG QRS Complex detection.
• To implement the system on a Field Programmable Gate Array (FPGA) for enhanced performance.
• To evaluate the accuracy and resource efficiency of the proposed design.

Main Methods:

• Development of an algorithm integrating Mathematical Morphology and Quadratic Spline wavelet transform.
• Design of a parallel and pipelined hardware architecture for real-time processing.
• Implementation on FPGA, including Morphological filtering, Quadratic Spline wavelet transform, and Modulus Maxima Pair Recognition modules.

Main Results:

• The developed system achieves a maximum operating frequency of 35MHz with a throughput of one sample per clock cycle.
• Accurate QRS Complex detection performance was validated using the MIT/BIH arrhythmia database.
• Detailed reporting of resource consumption for the FPGA implementation.

Conclusions:

• The proposed algorithm and FPGA-based hardware architecture offer a computationally efficient solution for ECG QRS Complex detection.
• The design is suitable for high-volume data processing and real-time applications on portable devices.
• This advancement contributes to improved cardiac monitoring technologies.