Automatic QRS Detection and Segmentation Using Short Time Fourier Transform and Feature Fusion

Abdullah Biran; Aleksandar Jeremic; IEEE

doi:10.1109/CCECE47787.2020.9255676

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Automatic QRS Detection and Segmentation Using Short Time Fourier Transform and Feature Fusion

Abdullah Biran, Aleksandar Jeremic and IEEE

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vol.2020-, pp.1-4

30/08/2020

DOI: https://doi.org/10.1109/CCECE47787.2020.9255676

Abstract

Bio-identification

Classification algorithms

ECG

ECG Features

Electrocardiography

Feature extraction

Fourier transforms

Heart beat

Kalman filters

Mathematical model

QRS Detection and segmentation

STFT

QRS detection from an electrocardiogram (ECG) is potentially useful tool in many applications such as diagnosing cardiac diseases, bio-identification, bio-encryption, etc. In this paper, we present an automated algorithm for detecting QRS waves and segmenting ECG signal into separate beats using short time Fourier transform (STFT) and multi-channel ECG feature-based classification. We test the performance of our algorithm using ECG signals of 62 subjects from the ECG ID public database. The results show that our method is capable of extracting QRS waves with 99.45% average QRS segmentation accuracy.

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Details

Title: Automatic QRS Detection and Segmentation Using Short Time Fourier Transform and Feature Fusion
Creators - without role: Abdullah Biran - McMaster University
Aleksandar Jeremic - McMaster University
IEEE
Publication Details: 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vol.2020-, pp.1-4
Publisher: IEEE
Identifiers: 9920011008331
Academic Unit: King Faisal University
Language: English
Resource Type: Conference proceeding