{'fraction_non_alphanumeric': 0.021538461538461538, 'fraction_numerical': 0.0, 'mean_word_length': 5.062111801242236, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Stress level evaluation using BP Neural network based on time-frequency analysis of HRV

Physiological stress is human body's response to a challenge by multiple systems in the body, in which ANS (Autonomic Nervous System) plays key roles. This paper proposes a stress level evaluation method based on time-frequency domain analysis of HRV (Heart Rate Variability) features combining statistical optimization. The most reliable and efficient indicator to ANS is HRV features obtained from time and frequency domain analysis of R-R intervals recorded during the modified Stroop test. A BP Neural network is utilized to train HRV features selected by t-test and one-way Anova test and classify new samples into four stress level categories i.e. relaxed state, low stress, medium stress and high stress. Weighing factors in classification and number of neurons in hidden layer are considered for optimized network. The proposed method has been validated by experimental results.