Evaluation of lead i ECG features discriminant power for cardiac diseases identification

Renato Pereira; Pedro Rodrigues; Bruno C. Bispo

Evaluation of lead i ECG features discriminant power for cardiac diseases identification

Renato Pereira
, Pedro Rodrigues
, Bruno C. Bispo

Research output: Contribution to conference › Paper

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Abstract

This work proposes to analyze the capacity of several ECG features ofLead I to discriminate 28 pairs of study groups, combining 7 patholog-ical groups and 1 control group, presented in the PTB Diagnostic ECGDatabase. For each pair, it was achieved an accuracy between 66.7% and96.9% using feature selection algorithm and SVM classifiers.

Original language	English
Pages	113-114
Number of pages	2
Publication status	Published - 2019
Event	RECPAD 2019 - 25th Portuguese Conference on Pattern Recognition - Porto, Portugal Duration: 31 Oct 2019 → 31 Oct 2019 Conference number: 25

Conference

Conference	RECPAD 2019 - 25th Portuguese Conference on Pattern Recognition
Country/Territory	Portugal
City	Porto
Period	31/10/19 → 31/10/19

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28391054Final published version, 1.22 MBLicence: Unspecified

http://hdl.handle.net/10400.14/33149Licence: Unspecified

Cite this

@conference{4b0b586a874046fb85fc2aa89b81acf3,

title = "Evaluation of lead i ECG features discriminant power for cardiac diseases identification",

abstract = "This work proposes to analyze the capacity of several ECG features ofLead I to discriminate 28 pairs of study groups, combining 7 patholog-ical groups and 1 control group, presented in the PTB Diagnostic ECGDatabase. For each pair, it was achieved an accuracy between 66.7\% and96.9\% using feature selection algorithm and SVM classifiers.",

author = "Renato Pereira and Pedro Rodrigues and Bispo, \{Bruno C.\}",

year = "2019",

language = "English",

pages = "113--114",

note = "RECPAD 2019 - 25th Portuguese Conference on Pattern Recognition ; Conference date: 31-10-2019 Through 31-10-2019",

}

TY - CONF

T1 - Evaluation of lead i ECG features discriminant power for cardiac diseases identification

AU - Pereira, Renato

AU - Rodrigues, Pedro

AU - Bispo, Bruno C.

N1 - Conference code: 25

PY - 2019

Y1 - 2019

N2 - This work proposes to analyze the capacity of several ECG features ofLead I to discriminate 28 pairs of study groups, combining 7 patholog-ical groups and 1 control group, presented in the PTB Diagnostic ECGDatabase. For each pair, it was achieved an accuracy between 66.7% and96.9% using feature selection algorithm and SVM classifiers.

AB - This work proposes to analyze the capacity of several ECG features ofLead I to discriminate 28 pairs of study groups, combining 7 patholog-ical groups and 1 control group, presented in the PTB Diagnostic ECGDatabase. For each pair, it was achieved an accuracy between 66.7% and96.9% using feature selection algorithm and SVM classifiers.

M3 - Paper

SP - 113

EP - 114

T2 - RECPAD 2019 - 25th Portuguese Conference on Pattern Recognition

Y2 - 31 October 2019 through 31 October 2019

ER -

Evaluation of lead i ECG features discriminant power for cardiac diseases identification

Abstract

Conference

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