###### Abstract  
Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features 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, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. 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. elecrocardiogram; feature extraction; heart rate variability; machine learning; major depression +
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A timely and precise diagnosis is critical for effective prevention and treatment, given that depression is a significant risk factor for other serious health conditions such as heart failure [10], dementia [11], and diabetes [12]. Individuals with depression exhibit an increased likelihood 60% of developing additional illnesses and an increased risk of suicide 4% [2]. Furthermore, the economic burden imposed by depression is substantial, estimating 1 trillion USD annually on a global scale [13]. Thus, enhancing diagnostic accuracy is imperative to mitigate these impacts. Innovative methodologies for depression detection leverage physiological signals, notably biopotentials, which are imperative to elucidate the dynamics of tissues, nerves, and organs. These signals can furnish a quantitative index that conveys pertinent insights into a patient's condition. Integrating these signals with machine learning algorithms for precise classification emerges as a promising strategy to detect not only emotions and feelings, but also depression and anxiety. Within this framework, efforts to utilize electrocardiogram (ECG) and electroencephalogram (EEG) signals have produced various results. Metrics such as entropy [14; 15; 16], zero cross frequency, and frequency centroid [17] have been identified as significant in the detection of depressive patterns and emotionally affective signals associated with the parasympathetic system. In terms of emotion induction, methodologies include the use of autobiographical memories and visual stimulation [18], as well as script-driven imagery, which employs written narratives and imagination to evoke a robust emotional response [19; 20]. Numerous investigations have concentrated on the detection of depression using ECG data coupled with machine learning methodologies. The prevailing paradigm predominantly employs elementary machine learning classification algorithms, such as Support Vector Machines (SVM), in conjunction with the extraction of heart rate variability (HRV) attributes [21]. Scholars typically integrate more than 20 disparate attributes within a classification framework along with HRV-related characteristics [15]. A particular study exclusively examined HRV behavior alongside specific variables derived from a singular signal to evaluate depression, achieving metrics that surpass 80% with Bayesian classifiers [22]. A recent publication delineated the machine learning metrics that oscillate between 60% and 70% for the binary classification of subjects with and without depression using ECG-derived variables [23].