Research Article
Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation
@INPROCEEDINGS{10.1007/978-3-031-28663-6_9, author={Henriques Zacarias and Virginie Felizardo and Leonice Souza-Pereira and Andr\^{e} Pinho and Susana Ramos and Mehran Pourvahab and Nuno Garcia and Nuno Pombo}, title={Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation}, proceedings={IoT Technologies for HealthCare. 9th EAI International Conference, HealthyIoT 2022, Braga, Portugal, November 16-18, 2022, Proceedings}, proceedings_a={HEALTHYIOT}, year={2023}, month={3}, keywords={Gender classification nonstandard ECG Real data Machine learning}, doi={10.1007/978-3-031-28663-6_9} }- Henriques Zacarias
Virginie Felizardo
Leonice Souza-Pereira
André Pinho
Susana Ramos
Mehran Pourvahab
Nuno Garcia
Nuno Pombo
Year: 2023
Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation
HEALTHYIOT
Springer
DOI: 10.1007/978-3-031-28663-6_9
Abstract
This paper presents a comparison of nine models for gender identification using nonstandard ECG signal. Methods: QRS features, QT interval, RR interval, HRV features and HR were extracted from three minutes of 40 ECG’s (from 24 female and 16 males) available at ALLab dataset and 108 ECG’s (from 52 female and 56 males) available at CYBHi dataset. Models were developed using Decision tree, SVM, kNN, Boosted tree, Bagged tree, Subspace kNN, Subspace Discriminant, two majority vote and verified by external validation. Results: The study presented achieved as best results an accuracy of 78% from Boosted tree and 85% from majority vote. Conclusion: The automatic detection of gender by ECG could be very important and improve the development of predictive systems for cardiovascular disease. These classifications are promising due to the use of nonstandard ECG and to the simplicity of extraction of features that potentiated the correct classification
