Lifetime exposure to abuse, current stressors, and health in federally qualified health center patients

Richard, L; Hurst, T.; Lee, Joohee

doi:10.1080/10911359.2019.1573711

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…In recent years, sensor technologies have been significantly developed to help generate large data at a relatively low cost [1]. The fields of the Internet of Things (IoT) [2], precision medicine [3], and industry 4.0 [4] produce advanced, large temporally annotated data.…”

Section: Introductionmentioning

confidence: 99%

Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals

et al. 2023

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Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature’s 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification.

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Section: Introductionmentioning

confidence: 99%

Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals

et al. 2023

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“…There has been a notable increase in depression, anxiety, stress and other stress-related diseases, worldwide (1)(2)(3). Stress deteriorates the physical and mental well-being of a human.…”

Section: Introductionmentioning

confidence: 99%

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection

Iqbal

Elahi

Wijns

et al. 2022

Front. Med. Technol.

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Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.

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“…Essa reação faz com que o indivíduo sofra alterações psicofisiológicas e entre em estado de alerta [Selye 1950]. Apesar de ser um mecanismo de defesa essencial para a sobrevivência, o estado de estresse torna-se prejudicial à saúde quando há frequente, intensa e consistente exposição, resultando em vários malefícios físicos e mentais [Richard 2019;Everly e Lating 2019]. Portanto, o estresse deve ser diagnosticado em seus estágios iniciais com o intuito de não acarretar danos ou conduzir a pessoa a um estado crônico.…”

Section: Introductionunclassified

Um Modelo de Aprendizado Profundo Multimodal para Classificação de Estresse Utilizando Sinais Obtidos por Dispositivos Vestíveis de Pulso

Medeiros¹,

Cunha²,

Santos³

et al. 2022

Anais Do XXII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2022)

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A recente proliferação dos dispositivos vestíveis (e.g. smartphones e smartwatches) e sua capacidade de medir vários sinais fisiológicos criam uma oportunidade de monitoramento contínuo e discreto do estado de estresse de um indivíduo. Tendo isso em vista, este trabalho propõe um novo modelo para classificação de estresse baseado em uma rede de convolução usando dados coletados por dispositivos de pulso. Os resultados obtidos, utilizando a validação leave-one-subject-out (LOSO), alcançaram a taxa média de 95,67% de acurácia, sendo um valor superior à maioria dos trabalhos já existentes na literatura.

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Lifetime exposure to abuse, current stressors, and health in federally qualified health center patients

Cited by 4 publications

References 31 publications

Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals

Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection

Um Modelo de Aprendizado Profundo Multimodal para Classificação de Estresse Utilizando Sinais Obtidos por Dispositivos Vestíveis de Pulso

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