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Machine learning approach for classifying and predicting depressive behavior based on PPG an ECG feature extraction
  • +1
  • mateo alzate,
  • Robinson Torres,
  • Jose de la roca,
  • Martha Hernandez
mateo alzate
Universidad EIA

Corresponding Author:[email protected]

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Robinson Torres
Universidad EIA
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Jose de la roca
Universidad de Guanajuato
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Martha Hernandez
Hospital Regional de Alta Especialidad del Bajio
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The creation of a system for depression detection is proposed, based on the acquisition and processing of ECG and PPG signals, followed by the development of an algorithm for pattern classification and detection. The main goal is to achieve an accurate classification of an individual into a depression or non-depression group, ultimately achieving the correct detection of the mentioned problem. This was accomplished through the analysis of a set of physiological variables taken from PPG and ECG signals, using an experimental protocol called script-driven imagery adapted to the current paradigm, applied to individuals from the High Specialized Medical Center of Bajio T1 in Leon, Mexico. The variables to be used include heart rate variability, pulse transit time, heart rate, respiration signal, physiological coherence of each, and the global coherence index, as well as other frequency-related variables selected based on a literature review. Subsequently, a Python program was developed for processing and obtaining the values of the mentioned variables for later use in a machine learning code. A multi machine learning model test was carried just to find out that the binary classification algorithm that yielded the best performance was a Random Forest, with a sensitivity and accuracy of 76% for the validation group, although higher percentages were achieved with smaller groups of individuals. A review of the performance of the best features in the algorithm was also conducted to identify which variables can have a greater impact when attempting to detect a depressive state in an individual.