# Sex Recognition through ECG Signals aiming toward Smartphone Authentication
## 5 Results  
We show seven different and independent experiments following the architecture of Figure 6. We started from single leads (X, Y, Z), then with the possible set of combinations (XY, XZ, YZ, XYZ). The results are separated into two parts, single lead and mixed lead analysis.  
From the accuracy perspective, X lead reaches an 87.3% \(\pm\) 4.4%, Y lead 84.6% \(\pm\) 4.0%, and Z lead 86.6% \(\pm\) 4.0%. Figure 7 consolidates the results set, and by bin, the biggest score is marked with a red dot. Under bipolar configuration, X lead provides better performance, being positioned in 7 of all 10 bins, compared to Y and Z leads with 1/10 and 2/10, respectively.  
Taking only the best results by bin, the general sensitivity climbs to 89.6% \(\pm\) 4.5%, specificity to 86.2% \(\pm\) 8.1%, precision to 86.8% \(\pm\) 6.3%, and the accuracy to 87.9% \(\pm\) 4.1 %. Figure 8 consolidates the confusion matrix derivation results of the classification.  
On the other hand, the combination of the leads XYZ achieved an accuracy of 94.4% \(\pm\) 2.0%, which is an improvement of 7.1 points over the bipolar X lead. The XY leads combination achieved an accuracy of 90.3% \(\pm\) 3.6%, XZ leads 92.8% \(\pm\) 2.2%, and YZ leads 89.3% \(\pm\) 2.8%. In 9 of 10 bins, the XYZ signals were the best scheme (Figure 9).  
With the best values by bin, the classification rate contains the following results: sensitivity of 94.4% \(\pm\) 3.6%, specificity of 94.5% \(\pm\) 3.8%, precision of 94.4% \(\pm\) 3.4%, and accuracy 94.4% \(\pm\) 2.0%. The results in Figures 8 and 10 are subtracted from each other and compared in Figure 11. As a result, the sensitivity achieved an advance of [12.7%, 3.5%, 9.7%, 3.6%, 12.0%, 5.6%, 5.5%] in [\(B1\), \(B3\), \(B4\), \(B6\), \(B7\), \(B9\), \(B10\)] but a decrement of [1.8%, 2.5%, 0.4% ] in [\(B2\), \(B5\), \(B8\)]. The specificity achieved an increment of [7.3%, 17.9%, 14.4%, 5.9%, 9.4%, 18.8%, 8.2%, 4.0%] in [\(B1\), \(B2\), \(B3\), \(B4\), \(B5\), \(B6\), \(B8\), \(B9\)] but a decrement of [2.1%, 0.9%] in [\(B7\), \(B10\)]. The precision achieved an improvement of [7.2%, 14.4%, 12.0%, 6.5%, 9.4%, 15.5%, 8.0%, 4.8 %] in [\(B1\), \(B2\), \(B3\), \(B4\), \(B5\), \(B6\), \(B8\), \(B9\)] but a decrement of [1.3%, 0.8%] in [\(B7\), \(B10\)]. The accuracy in all segments achieved an improvement of [9.9%, 8.2%, 9.0%, 7.8%, 3.5%, 11.3%, 4.8%, 4.1%, 4.7%, 1.9%] in [\(B1\), \(B2\), \(B3\), \(B4\), \(B5\), \(B6\), \(B7\), \(B8\), \(B9\), \(B10\)].  
Figure 7: Single lead accuracy results. (\(\bullet\) means maximum value per set).  
Figure 8: Single lead confusion matrix derivation results.  
Figure 10: Combined leads classification results.  
Figure 9: Combined leads accuracy results. (\(\bullet\) means maximum value per set).  
In synthesis, [B1, B3, B4, B6, B9] received a direct improvement in all their derivation values. The cases of B2 and B5 are similar; both received a decrement in sensitivity of 1.8% and 2.5%, with a score of 91.0% and 86.3%; in contrast, both achieved a significant improvement in specificity and precision with values of B2-[93.4%, 93.1%] and B5-[98.9%, 98.8%], respectively. On the other hand, B7 received a decrease in specificity and precision of 2.1% and 1.3% with an equal value of 92.5% and was supported with an increase of 12% in the sensitivity with a reach of 98%. B8 achieved 94.3% in their sensitivity, which represents a decrement of 0.4%, but an increase of 8.2%, 8.0%, and 4.1% in their specificity, precision, and accuracy with final values of 94.7%, 93.2%, and 96.6%. Finally, B10 increased by 5.5% and 1.9% in the sensitivity and accuracy values to 96.5% and 95.5% and reduced by 0.9% and 0.8% in the specificity and precision with final values of 94.7% and 93.7%. We present the general classification metrics of the experiment in Table 2.