Abstract
The present work proposes an algorithm to calculate the QRS Score and the determination of confounders starting from Electrocardiographic (ECG) signals. The QRS Score is a parameter that indicates how big the scar is in the wall of the patient’s myocardium; It is also helpful in determining how healthy the heart is. Said parameter is calculated from signal information such as time measurements, amplitude relationships and waveforms. The evaluation of the ECG signals is usually done by visual perception of the graph paper where it is printed as a result of the electrocardiogram examination. However, the reproducibility of this method is 60% and the repeatability is 66%. This definitely affects the accuracy of the score obtained and therefore the diagnosis of a disease. The proposed algorithm aims to reduce the subjectivity of the analysis and standardize the punctuations to be obtained. The algorithm is made up of processing stages that involve the conditioning of the signal using finite impulse response (FIR) filters, decontamination of confounders by neural networks, detection of the QRS complex, detection of times and amplitudes and finally obtaining the QRS score from a table of criteria. Finally, the proposed algorithm obtained a reproducibility of 75% and a repeatability of 100% exceeding the performance of the specialist.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 4th Brazilian Technology Symposium (BTSym’18) - Emerging Trends and Challenges in Technology |
| Editors | Osamu Saotome, Rangel Arthur, Vânia Vieira Estrela, Yuzo Iano, Hermes José Loschi |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 201-211 |
| Number of pages | 11 |
| ISBN (Print) | 9783030160524 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 4th Brazilian Technology Symposium, BTSym 2018 - Campinas, Brazil Duration: Oct 23 2018 → Oct 25 2018 |
Publication series
| Name | Smart Innovation, Systems and Technologies |
|---|---|
| Volume | 140 |
| ISSN (Print) | 2190-3018 |
| ISSN (Electronic) | 2190-3026 |
Conference
| Conference | 4th Brazilian Technology Symposium, BTSym 2018 |
|---|---|
| Country | Brazil |
| City | Campinas |
| Period | 10/23/18 → 10/25/18 |
Fingerprint
Keywords
- Chagas
- Confounder
- ECG signals
- Neural networks
- QRS score
- Signal processing
- Waveforms
ASJC Scopus subject areas
- Decision Sciences(all)
- Computer Science(all)
Cite this
An algorithm to obtain the QRS score based on ECG parameters detection and neural networks for confounder classification. / Cabanillas, Julio; Tello, Gustavo; Mercado, Brandon; Kemper, Guillermo; Zimic, Mirko; Gilman, Robert.
Proceedings of the 4th Brazilian Technology Symposium (BTSym’18) - Emerging Trends and Challenges in Technology. ed. / Osamu Saotome; Rangel Arthur; Vânia Vieira Estrela; Yuzo Iano; Hermes José Loschi. Springer Science and Business Media Deutschland GmbH, 2019. p. 201-211 (Smart Innovation, Systems and Technologies; Vol. 140).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An algorithm to obtain the QRS score based on ECG parameters detection and neural networks for confounder classification
AU - Cabanillas, Julio
AU - Tello, Gustavo
AU - Mercado, Brandon
AU - Kemper, Guillermo
AU - Zimic, Mirko
AU - Gilman, Robert
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The present work proposes an algorithm to calculate the QRS Score and the determination of confounders starting from Electrocardiographic (ECG) signals. The QRS Score is a parameter that indicates how big the scar is in the wall of the patient’s myocardium; It is also helpful in determining how healthy the heart is. Said parameter is calculated from signal information such as time measurements, amplitude relationships and waveforms. The evaluation of the ECG signals is usually done by visual perception of the graph paper where it is printed as a result of the electrocardiogram examination. However, the reproducibility of this method is 60% and the repeatability is 66%. This definitely affects the accuracy of the score obtained and therefore the diagnosis of a disease. The proposed algorithm aims to reduce the subjectivity of the analysis and standardize the punctuations to be obtained. The algorithm is made up of processing stages that involve the conditioning of the signal using finite impulse response (FIR) filters, decontamination of confounders by neural networks, detection of the QRS complex, detection of times and amplitudes and finally obtaining the QRS score from a table of criteria. Finally, the proposed algorithm obtained a reproducibility of 75% and a repeatability of 100% exceeding the performance of the specialist.
AB - The present work proposes an algorithm to calculate the QRS Score and the determination of confounders starting from Electrocardiographic (ECG) signals. The QRS Score is a parameter that indicates how big the scar is in the wall of the patient’s myocardium; It is also helpful in determining how healthy the heart is. Said parameter is calculated from signal information such as time measurements, amplitude relationships and waveforms. The evaluation of the ECG signals is usually done by visual perception of the graph paper where it is printed as a result of the electrocardiogram examination. However, the reproducibility of this method is 60% and the repeatability is 66%. This definitely affects the accuracy of the score obtained and therefore the diagnosis of a disease. The proposed algorithm aims to reduce the subjectivity of the analysis and standardize the punctuations to be obtained. The algorithm is made up of processing stages that involve the conditioning of the signal using finite impulse response (FIR) filters, decontamination of confounders by neural networks, detection of the QRS complex, detection of times and amplitudes and finally obtaining the QRS score from a table of criteria. Finally, the proposed algorithm obtained a reproducibility of 75% and a repeatability of 100% exceeding the performance of the specialist.
KW - Chagas
KW - Confounder
KW - ECG signals
KW - Neural networks
KW - QRS score
KW - Signal processing
KW - Waveforms
UR - http://www.scopus.com/inward/record.url?scp=85068612521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068612521&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16053-1_19
DO - 10.1007/978-3-030-16053-1_19
M3 - Conference contribution
AN - SCOPUS:85068612521
SN - 9783030160524
T3 - Smart Innovation, Systems and Technologies
SP - 201
EP - 211
BT - Proceedings of the 4th Brazilian Technology Symposium (BTSym’18) - Emerging Trends and Challenges in Technology
A2 - Saotome, Osamu
A2 - Arthur, Rangel
A2 - Vieira Estrela, Vânia
A2 - Iano, Yuzo
A2 - Loschi, Hermes José
PB - Springer Science and Business Media Deutschland GmbH
ER -