A proof-of-concept investigation of multi-modal physiological signal responses to acute mental stress

Parreira, Jesse D.; Chalumuri, Yekanth Ram; Mousavi, Azin; Modak, Mukund J.; Zhou, Yuanyuan; Sanchez-Perez, Jesus Antonio; Gazi, Asim H.; Harrison, Anna B.; Inan, Omer T.; Hahn, Jin‐Oh

doi:10.1016/j.bspc.2023.105001

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…In the CT section, the subject was cognitively stimulated through two cognitive tests: the Stroop test [ 48 ] and the n-back test [ 49 , 50 ]. Both tests were executed through a custom-designed graphical user interface (GUI) structured to make a gradual augmentation in the level of complexity.…”

Section: Methodsmentioning

confidence: 99%

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Valerio,

Demarchi,

O’Flynn

et al. 2024

Sensors

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Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

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Section: Methodsmentioning

confidence: 99%

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Valerio,

Demarchi,

O’Flynn

et al. 2024

Sensors

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show abstract

“…The existing body of literature has exploited HR and HRV in the studies of cardiovascular health and disease (Stein et al 2007, Haensel et al 2008, Thayer et al 2010, Hillebrand et al 2013, Soares-Miranda et al 2014, physical fitness (Plews et al 2013, Mongin et al 2022, mental health (Kemp and Quintana 2013, Quintana and Heathers 2014, Beauchaine and Thayer 2015, Pham et al 2021, and cognitive impairments (Luft et al 2009, Quintana et al 2012, Forte et al 2019. The use of HR and HRV in research and practice is expected to rapidly expand given the remarkable advances in wearable sensing technology to conveniently measure the physiological signals bearing HR and HRV, including electrocardiogram (ECG) (Sieciński et al 2020, Pham et al 2021, Parreira et al 2023, photoplethysmogram (PPG) (Lu et al 2008, Uçar et al 2018, and seismocardiogram (SCG) (Hurnanen et al 2017, Sieciński et al 2020 and ballistocardiogram (BCG) (Shin et al 2011, Brüser et al 2013 to list a few.…”

Section: Introductionmentioning

confidence: 99%

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram

Zhou,

Lindsey,

Snyder

et al. 2024

Physiol. Meas.

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Objective: To develop analytical formulas which can serve as quantitative guidelines for the selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart rate (HR) and heart rate variability (HRV) with a desired level of accuracy. Approach: We developed analytical formulas which relate the ECG sampling rate to conservative bounds on HR and HRV errors: (i) one relating HR and sampling rate to a HR error bound and (ii) the others relating sampling rate to HRV error bounds (in terms of root-mean-square of successive differences (RMSSD) and standard deviation of normal sinus beats (SDNN)). We validated the formulas using experimental data collected from 58 young healthy volunteers which encompass a wide HR and HRV ranges through strenuous exercise. Main results: The results strongly supported the validity of the analytical formulas as well as their tightness. The formulas can be used to (i) predict an upper bound of inaccuracy in HR and HRV for a given sampling rate in conjunction with HR and HRV as well as to (ii) determine a sampling rate to achieve a desired accuracy requirement at a given HR or HRV (or its range). Significance: HR and its variability (HRV) derived from the ECG have been widely utilized in a wide range of research in physiology and psychophysiology. However, there is no established guideline for the selection of the sampling rate for the ECG required to calculate HR and HRV with a desired level of accuracy. Hence, the analytical formulas may guide in selecting sampling rates for the ECG tailored to various applications of HR and HRV.

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Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

Li,

Zhu,

Guan

et al. 2024

Stress and Health

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Earthquakes, as significant natural disasters, still cannot be accurately predicted today. Although current earthquake early warning systems can provide alerts several seconds in advance, acute stress responses (ASR) in emergency situations can waste these precious escape seconds. To investigate the correlation between personality and ASR, this study collected the temperament and character of all participants using the Chen Huichang‐60 Temperament Scale and the DISC Personality Inventory. In addition, this study simulated growing earthquakes in an earthquake experience hall, collecting heart rate variability and respiration signal variations throughout the process from subjects. Multivariate analysis of variance (MANOVA) and Toeplitz Inverse Covariance‐Based Clustering methods were used to analyse the differences and connections between them. Furthermore, this study employed a deep learning model that combines Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) to predict ASR across personalities. This model used datasets from the majority dataset of a certain personality and a single participant, respectively, and showed different performance. The results are as follows. After categorising participants based on personality test results, MANOVA revealed significant differences between the personality groups Influence‐Choleric and Influence‐Sanguine (p = 0.001), Influence‐Phlegmatic and Steadiness‐Sanguine (p = 0.023), Influence‐Sanguine and Steadiness‐Sanguine (p < 0.001) and Influence‐Sanguine and Steadiness‐Phlegmatic (p < 0.001), as well as across different earthquake stages (p < 0.01). The clustering method quantified stress responses over time for different personalities and labelled ASR levels for use in supervised learning. Ultimately, the CNN‐LSTM model performed predictions of ASR using both personality and individual datasets, achieving the AUC of 0.795 and 0.72, demonstrating better prediction and classification effectiveness with the former. This study provides a new personality‐based method for earthquake stress management, creating possibilities for longitudinal stress research and prediction. It aids the general public in comprehending their own acute stress and allows authorities and communities to make practical, efficient disaster evacuation plans based on the overall situation of public ASR.

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A proof-of-concept investigation of multi-modal physiological signal responses to acute mental stress

Cited by 7 publications

References 38 publications

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram

Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

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