A nano-watt ECG feature extraction engine in 65-nm technology

Temesghen Tekeste; Hani Saleh; Baker Mohammad; Ahsan Khandoker; Mohammed Ismail

doi:10.1109/TCSII.2017.2658670

A nano-watt ECG feature extraction engine in 65-nm technology

Temesghen Tekeste
, Hani Saleh
, Baker Mohammad
, Ahsan Khandoker
, Mohammed Ismail

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

This brief presents an ultralow power electrocardiography (ECG) feature extraction engine. ECG signal represents the cardiac cycle and contains key features, such as QRS complex, {P} -wave, and {T} -wave, which provide important diagnostic information about cardiovascular diseases. The ECG feature extraction is based on combined techniques of curve length transform (CLT) and discrete wavelet transform. A novel pipelined architecture for implementing CLT is proposed. The system was fabricated using GF-65-nm technology and consumed 642 nW only when operating at a frequency of 7.5 kHz from a supply voltage of 0.6 V. Ultralow power consumption of the SoC made it suitable for self-powered wearable devices.

Original language	British English
Article number	7833083
Pages (from-to)	1099-1103
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	65
Issue number	8
DOIs	https://doi.org/10.1109/TCSII.2017.2658670
State	Published - Aug 2018

Keywords

Curve length transform (CLT)
discrete wavelet transform (DWT)
electrocardiography (ECG) features
ultralow power

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TCSII.2017.2658670

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title = "A nano-watt ECG feature extraction engine in 65-nm technology",

abstract = "This brief presents an ultralow power electrocardiography (ECG) feature extraction engine. ECG signal represents the cardiac cycle and contains key features, such as QRS complex, \{P\} -wave, and \{T\} -wave, which provide important diagnostic information about cardiovascular diseases. The ECG feature extraction is based on combined techniques of curve length transform (CLT) and discrete wavelet transform. A novel pipelined architecture for implementing CLT is proposed. The system was fabricated using GF-65-nm technology and consumed 642 nW only when operating at a frequency of 7.5 kHz from a supply voltage of 0.6 V. Ultralow power consumption of the SoC made it suitable for self-powered wearable devices.",

keywords = "Curve length transform (CLT), discrete wavelet transform (DWT), electrocardiography (ECG) features, ultralow power",

author = "Temesghen Tekeste and Hani Saleh and Baker Mohammad and Ahsan Khandoker and Mohammed Ismail",

note = "Funding Information: Manuscript received November 1, 2016; accepted December 26, 2016. Date of publication January 25, 2017; date of current version July 31, 2018. This work was supported by the Mubadala-SRC Center of Excellence for Energy Efficient Electronic Systems Research under Contract 2013-HJ2440. This brief was recommended by Associate Editor A. J. Acosta. Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",

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AU - Mohammad, Baker

AU - Khandoker, Ahsan

AU - Ismail, Mohammed

N1 - Funding Information: Manuscript received November 1, 2016; accepted December 26, 2016. Date of publication January 25, 2017; date of current version July 31, 2018. This work was supported by the Mubadala-SRC Center of Excellence for Energy Efficient Electronic Systems Research under Contract 2013-HJ2440. This brief was recommended by Associate Editor A. J. Acosta. Publisher Copyright: © 2004-2012 IEEE.

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N2 - This brief presents an ultralow power electrocardiography (ECG) feature extraction engine. ECG signal represents the cardiac cycle and contains key features, such as QRS complex, {P} -wave, and {T} -wave, which provide important diagnostic information about cardiovascular diseases. The ECG feature extraction is based on combined techniques of curve length transform (CLT) and discrete wavelet transform. A novel pipelined architecture for implementing CLT is proposed. The system was fabricated using GF-65-nm technology and consumed 642 nW only when operating at a frequency of 7.5 kHz from a supply voltage of 0.6 V. Ultralow power consumption of the SoC made it suitable for self-powered wearable devices.

AB - This brief presents an ultralow power electrocardiography (ECG) feature extraction engine. ECG signal represents the cardiac cycle and contains key features, such as QRS complex, {P} -wave, and {T} -wave, which provide important diagnostic information about cardiovascular diseases. The ECG feature extraction is based on combined techniques of curve length transform (CLT) and discrete wavelet transform. A novel pipelined architecture for implementing CLT is proposed. The system was fabricated using GF-65-nm technology and consumed 642 nW only when operating at a frequency of 7.5 kHz from a supply voltage of 0.6 V. Ultralow power consumption of the SoC made it suitable for self-powered wearable devices.

KW - Curve length transform (CLT)

KW - discrete wavelet transform (DWT)

KW - electrocardiography (ECG) features

KW - ultralow power

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A nano-watt ECG feature extraction engine in 65-nm technology

Abstract

Keywords

UN SDGs

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