# Sex Recognition through ECG Signals aiming toward Smartphone Authentication
## 2 Related Work
Finally, an emerging area is biometric ECG. An application example is performed by [PERSON] et al., which developed a person identification CNN classification cross-validating nine different databases reaching a 95.6% accuracy in extracting attributes by spectral correlation [41]. Due to the expansion of this topic, we recommend the work of [PERSON] et al. [42], which presents a survey that covers deep supervised learning, deep semi-supervised learning, and deep unsupervised learning methods for the identification of people. Regarding ECG sex recognition, [PERSON] et al. provided the Physikalisch-Technische Bundesanstalt (PTB-XL), a database that contains 71 different annotations and 5 diagnostic super-classes. PTB-XL follows the standard communications protocol for computer-assisted electrocardiography (SCP-ECG) with the purpose of being a reference point for future research into the machine learning scope for different classification approaches in ECG [43]. Additionally, they seek to classify person sex with transfer learning and inception models, getting an accuracy of 84.9% with a ResNet pre-trained model. [PERSON] et al. 's work also focuses their direction on age prediction, diagnosis likelihood, and person stratification. The work proposed by [PERSON] et al. used a study of risk mortality by misclassified sex data with an increasing score of 1.4 times over people with ECG samples that were properly classified [44]. At the same time, they implemented a Deep Neural Network (DNN) for sex classification, achieved an interval confidence of 95%, an accuracy score of 89% for internal validation, and [80%, 82%] and [80%, 83%] for external validation, using two different databases. On the other hand, [PERSON] et al., based on SimCLR [45], implemented a system that automatically acquires information about the ECG signal for a pre-training stage known as PCLR, trained with around 3.2 million ECGs, using contrastive learning without the requirement to fine-tune the network [46]. They transform each sample into a lineal representation, and contrastive learning allows a self-supervised scheme for data without annotations that focuses on highlighting regions with similarity or distinction to be usable in new operations. The system focuses on age prediction, sex classification, and left ventricular hypertrophy (LVH) and atrial fibrillation (AF) detection passed through four steps: selection module, encoder, projection head, and contrastive loss function. For sex classification, with more than 5000 samples, it achieves an F1 score of 86%. It is also noteworthy that the contributions of [PERSON] et al. [2], [PERSON] et al. [4], and [PERSON] et al. [47] reached accuracies of 90.4%, 92.2%, and [91.3%, 86.3%], respectively. In Table 1, more indicators can be found regarding the mentioned work in this section. The advantage of our work in comparison with the related work is the significant accuracy score, taking into account variables such as: (i.) A minor number of electrodes on the person represents less complexity in the installation, increasing the user comfort. (ii.) Fewer signals to study. (iii.) Minor signal window acquisition. (iv.) Proposal of signal integration (v.) The nature of our analyzed database and our work classifies sex without taking into account the person's stance, while the related work mostly works with patients in resting position.