## 5 Conclusions  
In summary, this research has shown that depressive states in individuals can be detected with remarkable precision using ECG and PPG signals. The statistical analyzes performed elucidated distinctive characteristics with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states using a cross-validation approach, which supports the potential of biophysical data in increasing the diagnostic process of depression. The findings indicate that an enlarged sample size markedly improves the performance of the machine learning algorithm. Although a lower accuracy rate of 88% was observed with a combined dataset, the increased volume of information appears to improve its reliability.  
Compared to other studies, our methodology yields comparable results, demonstrating its efficacy even with a reduced number of test subjects and characteristics. The findings of this study further emphasize the importance of integrating multiple signal features and understanding the inherent phenomena underlying feature extraction. Although advanced deep learning models have exhibited superior performance, they require extensive datasets and often disregard the underpinning mechanisms of the features. Our approach, which encompasses a thorough analysis of signal characteristics, presents a viable alternative with substantial potential for enhancement with larger datasets.  
Furthermore, the variables in the frequency domain have proven effective in exploring associations with depression, implying that this methodology could be extended to other mental and behavioral disorders. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts. This quantitative method offers a significant advantage in the prevention, diagnosis, and treatment of socioeconomic disorders, addressing the challenges healthcare professionals face in managing these conditions.  
**Author Contributions:** Conceptualization, R.T., J.D.I.R., and M.H.; methodology, M.A., R.T., and A.Q.-Z.; software, M.A.; validation, M.A., A.Q.-Z., and M.H.; formal analysis, M.A. and J.D.I.R.; data curation, M.A. and R.T.; writing--original draft preparation, M.A. and A.Q.-Z.; writing--review and editing, J.D.I.R., R.T., A.Q.-Z., and M.H.; visualization, M.A.; supervision, R.T., J.D.I.R., and M.H.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of \"Comite local de investigacion en salut 1001\" (protocol code R-2023-1001-064 with date of approval 26 May 2023).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The databases acquired in this research are available for access upon request. Please contact [PERSON] via email for further information.  
**Acknowledgments:** Special thanks to the High Specialty Medical Center of the Bajio T1, the staff of the SPPSTIMS section, the research department, and finally, [PERSON] for allowing the use of the facility, providing the necessary materials, and supporting the research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.