### Stress Diagnosis  
To accurately and consistently measure stress levels, a method must involve analysis of specific electrophysiological, biochemical and psychometric parameters, monitoring the subject's development over time and examining the type and aspects of the stressor [18]. This work focuses exclusively on the electrophysiological parameters the photoplethysmographic (PPG) signal provides. In the last decade, the diagnosis and preliminary clinical analysis of an individual's state of health have been supported by non-invasive methods of monitoring biological signals [19]. The PPG signal deserves special attention, given its easy acquisition with affordable devices and the amount of physiological information it contains [20]. The PPG signal represents the volumetric variation experienced by the blood flow in the microvascular circulation of the tissues [21]. Photoplethysmography or photoplethysmogram (PPG) is an electro-optical technique that makes it possible to measure said volumetric variation in a barely invasive way and with minor sensitivity to the location of the sensors [20; 22]. The dynamics of blood flow or hemodynamics through the peripheral capillary network can be an ideal candidate to accurately detect the state of stress or relaxation (non-stress) of an individual. The first photoplethysmograph or pulse oximeter, attributed to the physiologist [PERSON], dates from 1937. Technological advances have led to the commercialization of increasingly economical pulse oximeters, of smaller size and lighter weight [23; 24], giving them tremendous versatility for their application in very different areas, such as health, sports or the agri-food industry [25]. Likewise, its use has been extended to the clinical environment to monitor physiological parameters related to the cardiorespiratory system [26]. Unlike other biological signals, which require bulky equipment or multiple accessories, such as the use of gels in EEG (Electroencephalogram) signals or electrodes in ECG (Electrocardiogram) signals or GSR (Galvanic Skin Response) signals, PPG signals use reduced electronics that have favored the proliferation of small pulse oximeters with low-cost sensors and easy integration with other smart devices [27]. In our technology-driven world, Big Data has seen a significant increase in capabilities due to the availability of tools for collecting vast amounts of data. Within the clinical field, there exist large public databases containing a vast number of biological samples from both healthy individuals and those with various pathologies. These samples are organized by age range for ease of access. It is common to come across simple technological solutions that use biological signals to communicate a subject's physiological characteristics. By conducting a thorough examination of biological signals, including the PPG signal, using advanced techniques, like those described in studies by [PERSON] et al. (2020), [PERSON] et al. (2022) and [PERSON] et al. (2022) [28; 29; 30], we expect to uncover more functional nuances of the physiological system that generates these signals over time. In order to fully comprehend how a complex system is functioning, it is crucial to examine the dynamic correlation structures and fluctuations, also known as intrinsic vibrations, in the data [31]. This requires techniques capable of detecting local and global details during dynamic transitions at various time scales, as highlighted by [32; 33; 34]. In recent years, the field of Deep Learning (DL) and its applications have broken into innumerable fields, such as natural language processing, information processing, more education, cybersecurity, robotics and control, among others. Many scientific findings support convolutional neural networks' (CNNs)' benefits, either via deep or shallow methods [35; 36].