RESUMEN
Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.
RESUMEN
BACKGROUND: Most mammary Paget disease (MPD) is associated with underlying in situ or invasive breast cancer. The objective of this study was to compare the clinicopathological characteristics and survival outcomes between breast cancer with Paget disease (PD) and breast cancer alone. METHODS: From the Surveillance, Epidemiology, and End Results (SEER) database, 2000-2015, of the US National Cancer Institute, we identified 1569 women who had PD with invasive ductal carcinoma (PD-IDC) and 1489 women who had PD with ductal carcinoma in situ (PD-DCIS). Independent demographic and clinicopathological variables as well as survival outcomes of these patients were compared to patients with the corresponding breast cancer without concomitant PD. RESULTS: PD-IDC and PD-DCIS both had worse survival outcomes and poorer tumor characteristics than the corresponding disease without PD. Contrary to in the breast cancer alone groups, in the breast cancer with PD groups, the HR status (P = 0.182 in PD-IDC and P = 0.371 in PD-DCIS), HER2 status (P = 0.788 in PD-IDC and P = 0.643 in PD-DCIS), and combined molecular subtype (P = 0.196 in PD-IDC and P = 0.853 in PD-DCIS) were not found to affect disease prognosis. After matching tumor characteristics and treatment approaches, PD-IDC as well as PD-DCIS exhibited no significant difference in disease prognosis with corresponding IDC and DCIS. Finally, by comparative analysis, a kind of PD-DCIS (ICD-O-3 code 8543/3) showed many invasive behaviors (31.8% of 8543/3 patients had stage I-III cancer) and was associated with worse survival outcomes than the other type of PD-DCIS. CONCLUSIONS: Breast cancer with concomitant PD was associated with more aggressive tumor characteristics and worse survival outcomes. The HR status, HER2 status, and combined molecular subtype could not affect the prognosis of breast cancer with PD. Moreover, a portion of the PD-DCIS cases were invasive breast cancer cases that required special treatment.