{'fraction_non_alphanumeric': 0.030526315789473683, 'fraction_numerical': 0.00631578947368421, 'mean_word_length': 5.65034965034965, '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}}
Automated Detection of Optimal DBS Device Settings

Continuous deep brain stimulation (DBS) of the ventral striatum (VS) is an effective treatment for severe, treatment-refractory obsessive-compulsive disorder (OCD). Optimal parameter settings are signaled by a mirth response of intense positive affect, which is subjectively identified by clinicians. Subjective judgments are idiosyncratic and difficult to standardize. To objectively measure mirth responses, we used Automatic Facial Affect Recognition (AFAR) in a series of longitudinal assessments of a patient treated with DBS. Pre- and post-adjustment DBS were compared using both statistical and machine learning approaches. Positive affect was significantly higher after DBS adjustment. Using XGBoost and SVM, the participant's pre- and post-adjustment responses were differentiated with accuracy values of 0.76 and 0.75, which suggest feasibility of objective measurement of mirth response.