ECG Segmentation Using Adaptive Hermite Functions

Péter Kovács; Carl Böck; Jens Meier; Mario Huemer

doi:10.1109/ACSSC.2017.8335601

ECG Segmentation Using Adaptive Hermite Functions

Péter Kovács
, Carl Böck
, Jens Meier
, Mario Huemer

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Electrical activity of the heart can be measured via electrodes placed on the human body resulting in the physiological signal called electrocardiogram (ECG). Each heartbeat contains elementary waves (P,QRS,T), which represent different phases of a cardiac cycle. The main characteristics of these waves such as amplitudes, durations or shapes are of great importance for medical experts. In this article, we develop an ECG delineation algorithm which extracts these features and additionally is able to track subtle variations of the elementary waves. To this end we propose an adaptive signal model based on Hermite functions, which is optimized for each heartbeat.

Original language	English
Title of host publication	Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers
Editors	Michael B. Matthews
Publisher	IEEE
Pages	1476-1480
Number of pages	5
ISBN (Electronic)	9781538618233
ISBN (Print)	978-1-5386-1823-3
DOIs	https://doi.org/10.1109/ACSSC.2017.8335601
Publication status	Published - Oct 2017

Fields of science

202 Electrical Engineering, Electronics, Information Engineering
202037 Signal processing
302032 Cardiology

JKU Focus areas

Computation in Informatics and Mathematics

Access to Document

10.1109/ACSSC.2017.8335601

Cite this

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title = "ECG Segmentation Using Adaptive Hermite Functions",

abstract = "Electrical activity of the heart can be measured via electrodes placed on the human body resulting in the physiological signal called electrocardiogram (ECG). Each heartbeat contains elementary waves (P,QRS,T), which represent different phases of a cardiac cycle. The main characteristics of these waves such as amplitudes, durations or shapes are of great importance for medical experts. In this article, we develop an ECG delineation algorithm which extracts these features and additionally is able to track subtle variations of the elementary waves. To this end we propose an adaptive signal model based on Hermite functions, which is optimized for each heartbeat.",

author = "P{\'e}ter Kov{\'a}cs and Carl B{\"o}ck and Jens Meier and Mario Huemer",

year = "2017",

month = oct,

doi = "10.1109/ACSSC.2017.8335601",

language = "English",

isbn = "978-1-5386-1823-3",

pages = "1476--1480",

editor = "Matthews, \{Michael B.\}",

booktitle = "Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers",

publisher = "IEEE",

}

TY - GEN

T1 - ECG Segmentation Using Adaptive Hermite Functions

AU - Kovács, Péter

AU - Böck, Carl

AU - Meier, Jens

AU - Huemer, Mario

PY - 2017/10

Y1 - 2017/10

N2 - Electrical activity of the heart can be measured via electrodes placed on the human body resulting in the physiological signal called electrocardiogram (ECG). Each heartbeat contains elementary waves (P,QRS,T), which represent different phases of a cardiac cycle. The main characteristics of these waves such as amplitudes, durations or shapes are of great importance for medical experts. In this article, we develop an ECG delineation algorithm which extracts these features and additionally is able to track subtle variations of the elementary waves. To this end we propose an adaptive signal model based on Hermite functions, which is optimized for each heartbeat.

AB - Electrical activity of the heart can be measured via electrodes placed on the human body resulting in the physiological signal called electrocardiogram (ECG). Each heartbeat contains elementary waves (P,QRS,T), which represent different phases of a cardiac cycle. The main characteristics of these waves such as amplitudes, durations or shapes are of great importance for medical experts. In this article, we develop an ECG delineation algorithm which extracts these features and additionally is able to track subtle variations of the elementary waves. To this end we propose an adaptive signal model based on Hermite functions, which is optimized for each heartbeat.

UR - https://www.scopus.com/pages/publications/85050979599

U2 - 10.1109/ACSSC.2017.8335601

DO - 10.1109/ACSSC.2017.8335601

M3 - Conference proceedings

SN - 978-1-5386-1823-3

SP - 1476

EP - 1480

BT - Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers

A2 - Matthews, Michael B.

PB - IEEE

ER -

ECG Segmentation Using Adaptive Hermite Functions

Abstract

Fields of science

JKU Focus areas

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Other files and links

Morphology based Cardiac Signal Processing

Cite this

ECG Segmentation Using Adaptive Hermite Functions

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

Morphology based Cardiac Signal Processing

Cite this