Publication detail

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals

HOLMES, D. PINTO, S. FELTON, C. SMITAL, L. LEINVEBER, P. JURÁK, P. GILBERT, B. HAIDER, C.

Original Title

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals

English Title

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals

Type

conference paper

Language

en

Original Abstract

Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.

English abstract

Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.

Keywords

Time-frequency analysis, Biomedical monitoring, Electrocardiography, Algorithm design and analysis, Real-time systems

Released

11.07.2017

Publisher

IEEE

Location

Jeju Island, Korea

ISBN

978-1-5090-2809-2

Book

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Pages from

2598

Pages to

2601

Pages count

4

URL

Documents

BibTex


@inproceedings{BUT143710,
  author="David {Holmes} and Samuel Cerqueira {Pinto} and Christopher L. {Felton} and Lukáš {Smital} and Pavel {Leinveber} and Pavel {Jurák} and Barry {Gilbert} and Clifton {Haider}",
  title="Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals",
  annote="Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.",
  address="IEEE",
  booktitle="2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
  chapter="143710",
  doi="10.1109/EMBC.2017.8037389",
  howpublished="online",
  institution="IEEE",
  year="2017",
  month="july",
  pages="2598--2601",
  publisher="IEEE",
  type="conference paper"
}