<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit.</p></abstract>

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit.

A FPGA system for QRS complex detection based on Integer Wavelet Transform

Faculty of Electrical Engineering, University of Montenegro, Džordža Vašingtona bb, 81000, Podgorica, Montenegro

University of Manchester, Oxford Road, M13 9PL, Manchester, United Kingdom

Technical Faculty Čačak, University of Kragujevac, Serbia

Measurement Science Review

{"article-title":"A FPGA system for QRS complex detection based on Integer Wavelet Transform"}

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a...

A FPGA system for QRS complex detection based on Integer Wavelet Transform

Publié en ligne: 21 sept. 2011

Pages: 131 - 138

DOI: https://doi.org/10.2478/v10048-011-0024-z

Mots clés
QRS detection, FPGA, Wavelet Transform

This content is open access.

A FPGA system for QRS complex detection based on Integer Wavelet Transform

Publié en ligne: 21 sept. 2011

Pages: 131 - 138

DOI: https://doi.org/10.2478/v10048-011-0024-z

Mots clésQRS detection, FPGA, Wavelet Transform

This content is open access.

Mots clés
QRS detection, FPGA, Wavelet Transform