Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.

INTRODUCTION: Advances in wearable sensor technology have enabled the collection of biomarkers that may correlate with levels of elevated stress. While significant research has been done in this domain, specifically in using machine learning to detect elevated levels of stress, the challenge of producing a machine learning model capable of generalizing well for use on new, unseen data remain. Acute stress response has both subjective, psychological and objectively measurable, biological components that can be expressed differently from person to person, further complicating the development of a generic stress measurement model. Another challenge is the lack of large, publicly available datasets labeled for stress response that can be used to develop robust machine learning models. In this paper, we first investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset to study the generalization capability of machine learning models built on larger datasets. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. In favor of reproducible research and to assist the community advance the field, we make all our experimental data and code publicly available through Github at https://github.com/xalentis/Stress. This paper's in-depth study of machine learning model generalization for stress detection provides an important foundation for the further study of stress response measurement using sensor biomarkers, recorded with wearable technologies. METHODS: Sensor biomarker data from six public datasets were utilized in this study. Exploratory data analysis was performed to understand the physiological variance between study subjects, and the complexity it introduces in building machine learning models capable of detecting elevated levels of stress on new, unseen data. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects, and applied feature engineering to generate additional features utilizing statistical summaries, with sliding windows of 25 s. We name this large dataset, StressData. In addition, we utilized random sampling on StressData combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects, which we name SynthesizedStressData. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it using Leave-One-Subject-Out (LOSO) validation, and on two additional, unseen publicly available stress biomarker datasets (WESAD and Toadstool). RESULTS: Our results show that previous models built on datasets containing a small number (<50) of subjects, recorded in single study protocols, cannot generalize well to new, unseen datasets. Our presented methodology for generating a large, synthesized training dataset by utilizing random sampling to construct scenarios closely aligned with experimental conditions demonstrate significant benefits. When combined with feature-engineering and ensemble learning, our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. The resulting model can be used as both a classification or regression predictor for estimating the level of perceived stress, when applied on specific sensor biomarkers recorded using a wearable device, while further allowing researchers to construct large, varied datasets for training machine learning models that closely emulate their exact experimental conditions. CONCLUSION: Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Machine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection. Feature-engineering assists in capturing these physiological variance, and this is further improved by utilizing ensemble techniques by combining the predictive power of different machine learning models, each capable of learning unique signals contained within the data. While there is a general lack of large, labeled public datasets that can be utilized for training machine learning models capable of accurately measuring levels of acute stress, random sampling techniques can successfully be applied to construct larger, varied datasets from these smaller sample datasets, for building robust machine learning models.

The effects of ketogenic metabolic therapy on mental health and metabolic outcomes in schizophrenia and bipolar disorder: a randomized controlled clinical trial protocol.

Background: Schizophrenia, schizoaffective disorder, and bipolar affective disorder are debilitating psychiatric conditions characterized by a chronic pattern of emotional, behavioral, and cognitive disturbances. Shared psychopathology includes the pre-eminence of altered affective states, disorders of thoughts, and behavioral control. Additionally, those conditions share epidemiological traits, including significant cardiovascular, metabolic, infectious, and respiratory co-morbidities, resulting in reduced life expectancy of up to 25 years. Nutritional ketosis has been successfully used to treat a range of neurological disorders and preclinical data have convincingly shown potential for its use in animal models of psychotic disorders. More recent data from open clinical trials have pointed toward a dramatic reduction in psychotic, affective, and metabolic symptoms in both schizophrenia and bipolar affective disorder. Objectives: to investigate the effects of nutritional ketosis via a modified ketogenic diet (MKD) over 14 weeks in stable community patients with bipolar disorder, schizoaffective disorder, or schizophrenia. Design: A randomized placebo-controlled clinical trial of 100 non-hospitalized adult participants with a diagnosis of bipolar disorder, schizoaffective disorder, or schizophrenia who are capable of consenting and willing to change their diets. Intervention: Dietitian-led and medically supervised ketogenic diet compared to a diet following the Australian Guide to Healthy Eating for 14 weeks. Outcomes: The primary outcomes include psychiatric and cognitive measures, reported as symptom improvement and functional changes in the Positive and Negative Symptoms Scale (PANSS), Young Mania Rating Scale (YMS), Beck Depression Inventory (BDI), WHO Disability Schedule, Affect Lability Scale and the Cambridge Cognitive Battery. The secondary metabolic outcomes include changes in body weight, blood pressure, liver and kidney function tests, lipid profiles, and markers of insulin resistance. Ketone and glucose levels will be used to study the correlation between primary and secondary outcomes. Optional hair cortisol analysis will assess long-term stress and variations in fecal microbiome composition. Autonomic nervous system activity will be measured via wearable devices (OURA ring and EMBRACE wristband) in the form of skin conductance, oximetry, continuous pulse monitoring, respiratory rate, movement tracking, and sleep quality. Based on the encouraging results from established preclinical research, clinical data from other neurodevelopment disorders, and open trials in bipolar disorder and schizophrenia, we predict that the ketogenic metabolic therapy will be well tolerated and result in improved psychiatric and metabolic outcomes as well as global measures of social and community functioning. We additionally predict that a correlation may exist between the level of ketosis achieved and the metabolic, cognitive, and psychiatric outcomes in the intervention group.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review.

INTRODUCTION: Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress. OBJECTIVE: The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face. METHODS: This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2]. RESULTS: A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability. CONCLUSION: Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.