Affiliation:
1. Department of Telecommunications, Faculty of Engineering, Fundación Universitaria Compensar, Bogota 111311, Colombia
2. Department of Electronics, Faculty of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia
Abstract
Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which could compromise the classification. In this paper, with the intention of capturing heartbeat dynamics, we divided the heart rate into different intervals, creating a specialized identification model for each interval. The sexual differentiation for each model was performed with a deep convolutional neural network from images that represented the RGB wavelet transformation of ECG pseudo-orthogonal X, Y, and Z signals, using sufficient samples to train the network. Our database included 202 people, with a female-to-male population ratio of 49.5–50.5% and an observation period of 24 h per person. As our main goal, we looked for periods of time during which the classification rate of sex recognition was higher and the process was faster; in fact, we identified intervals in which only one heartbeat was required. We found that for each heart rate interval, the best accuracy score varied depending on the number of heartbeats collected. Furthermore, our findings indicated that as the heart rate increased, fewer heartbeats were needed for analysis. On average, our proposed model reached an accuracy of 94.82% ± 1.96%. The findings of this investigation provide a heartbeat acquisition procedure for ECG sex recognition systems. In addition, our results encourage future research to include sex as a soft biometric characteristic in person identification scenarios and for cardiology studies, in which the detection of specific male or female anomalies could help autonomous learning machines move toward specialized health applications.
Funder
Fundacion Universitaria Compensar
Subject
Information Systems and Management,Computer Science Applications,Information Systems
Reference43 articles.
1. Little, W., and McGivern, R. (2022, June 18). Gender, Sex, and Sexuality. Available online: https://opentextbc.ca/introductiontosociology/chapter/chapter12-gender-sex-and-sexuality/.
2. Webster, M. (2022, June 18). Gender. Available online: https://www.merriam-webster.com/dictionary/gender.
3. Sex differences in human neonatal social perception;Connellan;Infant Behav. Dev.,2000
4. Sex differences in personality traits and gender-related occupational preferences across 53 nations: Testing evolutionary and social-environmental theories;Lippa;Arch. Sex. Behav.,2008
5. Nguyen, D.T., Kim, K.W., Hong, H.G., Koo, J.H., Kim, M.C., and Park, K.R. (2017). Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction. Sensors, 17.
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