Assessing the Effects of Stress Response on Glucose Variations

Sevil, Mert; Rashid, Mudassir; Hajizadeh, Iman; Maloney, Zacharie; Samadi, Sediqeh; Brandt, Rachel; Hobbs, Nicole; Park, Minsun; Quinn, Laurie; Çınar, Ali

doi:10.1109/bsn.2019.8771094

Cited by 11 publications

(15 citation statements)

References 8 publications

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“…For example, in diabetes, PA and APS may affect blood glucose concentrations in opposite directions. Signals from a wearable device would complement the information received from a continuous glucose monitoring device and provide advance information of the presence of PA and/or APS, which will ultimately affect the glucose level, enabling better insulin dosing decisions [6,9,[13][14][15][16]. The convenience of noninvasive wearable sensors means that the devices can be worn continuously in daily living to monitor the PA of users without hindering or limiting the motions of the users.…”

Section: Introductionmentioning

confidence: 99%

“…The stress hormones are difficult to measure continuously in free-living ambulatory conditions [7]. This necessitated research into surrogate biomarkers of APS, including readily measurable physiological biosignals such as heart rate (HR), respiration rate, pupil diameter, skin temperature (ST), electrodermal activity (EDA, also known as galvanic skin response (GSR)), blood volume pulse (BVP), electrocardiogram, and blood pressure [7,11,13,29,30]. The validity of these conveniently measured physiological biosignals for detecting APS has been demonstrated in several studies [7,11,13,19,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…This necessitated research into surrogate biomarkers of APS, including readily measurable physiological biosignals such as heart rate (HR), respiration rate, pupil diameter, skin temperature (ST), electrodermal activity (EDA, also known as galvanic skin response (GSR)), blood volume pulse (BVP), electrocardiogram, and blood pressure [7,11,13,29,30]. The validity of these conveniently measured physiological biosignals for detecting APS has been demonstrated in several studies [7,11,13,19,[29][30][31]. Previous works illustrated that reliably discriminating between different types of APS is possible, including emotional anxiety stress and mental stress [7,11,32,33].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed work consists of signals processing, feature extraction, data preparation, machine learning algorithm development, and evaluation of results (Figure 2). To achieve this aim, we designed experimental protocols to collect data, and we developed ML algorithms to detect APS in the presence of PA, which is a challenging problem as the readily-measurable physiological biosignals used to detect APS, such as HR and GSR, are also affected by PA [13]. The challenges in discriminating among different types of APS during periods of possibly simultaneous occurrences of PA are not only due to the overlapping responses in the physiological measurements, but also due to the noise and artifacts in the biosignals measured from wearable devices, which necessitates effective data filtering and adaptive noise cancellation (ANC) algorithms to extract and enhance the informative signal from the measurements for use in ML algorithms [7,13,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The detection and discrimination of simultaneous PA and APS can generate reliable digital biomarkers of the physiological and psychological states of people for use in the treatment of chronic conditions like diabetes and cardiovascular disease [34,35]. Diabetes treatment, in particular, can significantly benefit from more accurate assessments of the PA and APS states of people with Type 1 diabetes (T1D) [9,[13][14][15][16]. People with T1D must administer exogenous insulin to compensate for the loss of pancreatic insulin production and maintain their blood glucose concentration within the desired safe range [36].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Sevil

Rashid

Maloney

et al. 2020

Signals

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Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Sevil

Rashid

Maloney

et al. 2020

Signals

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Simulation model of a patient with type 1 diabetes using fuzzification

Zientarski,

Miłosz,

Nowicki

et al. 2023

J. Phys.: Conf. Ser.

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Type 1 diabetes is one of the most common diseases. The disease is caused by a lack of insulin secretion from the beta cells of the pancreas, which leads to improper regulation of blood glucose levels. The article presents a simulation model for determining changes in glucose-insulin levels using fuzzy logic techniques. The work concerns a quite simple deterministic simulation model of a digital twin of a type 1 diabetes patient, and fuzzification can significantly improve the efficiency of this model. A series of numerical experiments showed that enriching a simple deterministic patient model with a fuzzy approach gives much more accurate results than the simple deterministic model. The use of fuzzy sets opens up a number of possibilities and is a completely natural approach, resulting from, among others, the specificity of the simulated phenomenon - vital parameters of people with type 1 diabetes.

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Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems

et al. 2020

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Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

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Assessing the Effects of Stress Response on Glucose Variations

Cited by 11 publications

References 8 publications

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Simulation model of a patient with type 1 diabetes using fuzzification

Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems

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