# Sex Recognition through ECG Signals aiming toward Smartphone Authentication
## 2 Related Work  
This section intends to provide a scope of the work on sex recognition in ECG during the last few years, starting with an overview of Deep Learning in ECG, covering some examples of applications such as arrhythmias, sleep apnea, pregnancy, R peak detection, QRS complex recognition, cardiac rhythm, and human identification. Most of the work in Deep Learning in ECG is related to arrhythmia detection with the intention of avoiding human errors and supporting the processing of large databases for better diagnosis. [PERSON] et al. with a CNN and the database MIT-BIH differentiate normal beats of left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contractions (PVC), and premature atrial contractions (PAC) arrhythmias with a 98.07% of accuracy [29]. [PERSON] et al. [30], following arrhythmia detection, proposed the bagging model of a CNN-LSTM (long short-term memory) and RRHOS-LSTM (RR Interval and higher-order statistics) classifiers, each trained with different sub-samples, being both connected with an ANN meta-classifier and verified with a final CNN-LSTM model, allowing a general performance of 95.81%. [PERSON] et al. [31] proposed the Multimodal Image Fusion (MIF) and the Multimodal Feature Fusion (MFF) frameworks for arrhythmia and Myocardial Infarction, each receives the ECG signal transformed into Gramian Angular Field, Recurrence Plot, and Markov Transition Field images. MIF creates a new image fusing its image input for CNN classification, while MFF generates the fusion of the extracted features of the CNN penultimate layer for training a Support Vector Machine (SVM) for heartbeat classification. Finally, the model got an accuracy of 99.7% and 99.2% for arrhythmia and Myocardial Infarction. [PERSON] et al. [32], from an inter-patient perspective, transform the signal waveform and dynamics into a symbolic space in the MIT-BIH arrhythmia dataset to detect the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) using a multiperspective convolutional neural network with an overall accuracy of 96.4%. Due to the extension of this field, [PERSON] et al. [33] composed a review focused on the application of deep learning for the detection of arrhythmias. Covering the sleep apnea detection study, [PERSON] et al. [34] performed a comprehensive comparison between 14 conventional ML algorithms and deep learning approaches such as CNN, recurrent networks, and hybrid-CNN, having the best detection on hybrid deep models with an accuracy, sensitivity, and specificity of 88.13%, 84.26%, and 92.27%. For pregnancy ECG separation, [PERSON] et al. [35] seek to distinguish the ECG of the prenatal person due to the dominance of the abdominal maternal ECG signal, looking for early cardiac detection risks, decomposing the joint signal using the W-net deep learning architecture based on QRS complexes with a recall and precision of 98.23% and 99.3% with the abdominal and direct fECG database (ADFECGDB). For future analysis, there is an approach proposed by [PERSON] et al. to be tested that consists of the signal separation by IMF decomposition to recognize specific maternal and prenatal components by LSTM-RNN [36; 37]. In the R peak detection, the study of [PERSON] et al. is trained with wavelet scalograms from the ECG Heartbeat Categorization Dataset from Kaggle and evaluated by the MIT-BIH Arrhythmia Dataset, achieving an accuracy of 98.32% [38]. In the field of QRS findings, [PERSON] et al. [39], with the presence of noise in arrhythmia samples, categorize it with CNN and CRNN models, reaching an F1 score of 0.99 with four different databases, ensuring a generalizable model. [PERSON] et al. [40] classify the ECG rhythm with the spectrogram and signal waveform inputs with sequences of 5 seconds with noise presence and verified it with an external database, achieving an F1 score of 0.89 for discriminating Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Supraventricular Tachyarrhythmia (SVTA), Bigeminy (B), and Trigeminy (T) classes.