# Sex Recognition through ECG Signals aiming toward Smartphone Authentication
## 3 Materials and Methods
### Methodology  
Looking to cover the heartbeat dynamism, we adapt the histogram strategy to see the differences between male and female heartbeats based on their rhythm. Commonly, the RR interval oscillates from 0.6 to 1.2 s [50]. In this way, we defined the border values of the histogram. Then, with a bin width of 0.06 seconds, the total number of bins is ten. This value is calculated from the difference between the maximum and minimum values in the dataset divided by the width of the bin. Figure 3, with 9 million samples for each sex, helps to establish that both categories (male and female) possess similar RR tendencies, separated into 10 bins with seconds as the unit.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Acc.** & **Lead** & **Sample** & **Tech.** & **Fs** & **Position** & **Male-** & **Tr. \(\mid\)**Ts.** & **Sample** & **Year** \\  & **[\%]** & & **Length** & **[s]** & & **[Hz]** & **Position** & **Female** [\%]** & **Type** & **Type** \\ \hline
[2] & 90.4 & 12 & 10 & CNN & 500 & Supine & 52–48 & \(\sim\)500 k \(\mid\)\(\sim\)275 k & 2019 \\ \hline
[4] & 92.2 & 12 & N/A & DNN & N/A & N/A & 50.5–49.5 & \(\sim\)131 k \(\mid\)\(\sim\)68.5 k & 2021 \\ \hline
[47] & \begin{tabular}{c} DB1: \\ 91.3 \\ DB2: \\ 86.3 \\ \end{tabular} & 12 & 10 & SPAR \& KNN & \begin{tabular}{c} DB1: \\ 1000 \\ \end{tabular} & Resting & \
\begin{tabular}{c} DB1: \\ 60–40 \\ DB2: \\ 46–54 \\ \end{tabular} &
\begin{tabular}{c} DB1: \\ N = 0.104 k \\ DB2: \\ N = 8.9 k \\ \end{tabular} & 2021 \\ \hline
[43] & 84.9 & 12 & 10 & \
\begin{tabular}{c} CNN \\ xresnet \\ 1d101 \\ \end{tabular} & 100 & Resting & 52–48 &
\begin{tabular}{c} N = \(\sim\)22 k \\ 10 fold: \\ 8 \(\mid\)2 \\ \end{tabular} & 2021 \\ \hline
[44] & \begin{tabular}{c} valid. \\ int: 89 \\ ext: 81 \& 82 \\ \end{tabular} & 12 & 10 & DNN & 250 or 500 & Resting & \
\begin{tabular}{c} Tr. N/A \\ int. valid.: N/A \\ ext. valid. \\ \end{tabular} &
\begin{tabular}{c} Tr. \(\sim\)132 k \\ int. valid.: 68.5 k \\ ext. valid.: 7.7 k \\ \end{tabular} & 2022 \\ \hline
[46] & \begin{tabular}{c} f-score: \\ 87 \\ \end{tabular} & 12 & 10 & \begin{tabular}{c} PCLR \& 250 or \\ constrastive \\ learning \\ \end{tabular} & \
\begin{tabular}{c} 500 \\ 500 \\ \end{tabular} & Resting & N/A &
\begin{tabular}{c} N = \(\sim\)3229 k \\ 90\% \(\mid\)10\% \\ \end{tabular} & 2022 \\ \hline Own & 94.4 & 6 & 3.93 & CNN & 200 & Random & 51–49 & \(\sim\)3 k \(\mid\)\(\sim\)1.3 k & 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work about ECG sex identification with ML algorithms.  
Taking advantage of the previous distribution, we propose to discriminate each bin into a different classification model (Figure 4). This is to better differentiate the set of heartbeats. We assume that similar RRs have similar waveforms with the intention of helping the classifier performance.  
With the purpose of evaluating each signal input, we will develop the same experiment structure seven times but enable the ECG signal leads in different moments. As a result, the set of experiments will have the following sequence X, Y, Z, XY, XZ, YZ, XYZ.