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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F17%3A00484539" target="_blank" >RIV/68081731:_____/17:00484539 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EMBC.2017.8037389" target="_blank" >http://dx.doi.org/10.1109/EMBC.2017.8037389</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2017.8037389" target="_blank" >10.1109/EMBC.2017.8037389</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-1-5090-2810-8

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

2598-2601

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Jeju Island

Datum konání akce

11. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—