Stress monitoring using wearable sensors:a pilot study and stress-predict dataset

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring

Photoplethysmography-based respiratory rate estimation algorithm for health monitoring applications

PURPOSE: Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. METHODS: This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise. RESULTS: The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes. CONCLUSION: The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40846-022-00700-z

Heart Failure Association-International Cardio-Oncology Society Risk Score Validation in HER2-Positive Breast Cancer

Background: This paper looks to validate the risk score from the Heart Failure Association of the European Society of Cardiology and the International Cardio-Oncology Society (HFA-ICOS) for predicting potential cardiotoxicity from anticancer therapy for patients positive for human epidermal growth factor receptor 2. Methods: A total of 507 patients with at least five years since index diagnosis of breast cancer were retrospectively divided according to the HFA-ICOS risk proforma. According to level of risk, these groups were assessed for rates of cardiotoxicity via mixed-effect Bayesian logistic regression model. Results: A follow-up of five years observed cardiotoxicity of 3.3% (n = 3) in the low-risk, 3.3% (n = 10) in the medium-risk, 4.4% (n = 6) in the high-risk, and 38% (n = 6) in the very-high-risk groups respectively. For cardiac events related to treatment, the risk was significantly higher for the very-high-risk category of HFA-ICOS compared to other categories (Beta = 3.1, 95% CrI: 1.5, 4.8). For overall cardiotoxicity related to treatment, the area under the curve was 0.643 (CI 95%: 0.51, 0.76), with 26.1% (95% CI: 8%, 44%) sensitivity and 97.9% (95% CI: 96%, 99%) specificity. Conclusions: The HFA-ICOS risk score has moderate power in predicting cancer therapy–related cardiotoxicity in HER2-positive breast cancer patients