Blood Volume Pulse Research Articles

Physiological response to pain in patients with temporomandibular joint disorders during physiotherapy.

Pain assessment in patients with temporomandibular joint (TMJ) disorders during physiotherapy is a challenging yet desired task. Our study addresses two issues. First, we compare the pain perception in patients with TMJ disorders and a control group. The hand dynamometer is verified as an alternate pain self-report device in the second stage. For the dynamometer validation, we employed a cuff-pressure test to induce pain of various intensities. In the second stage, physiological signals (electrodermal activity and blood volume pulse) have been acquired during the physiotherapy in a control group and patients suffering from TMJ disorder. The linear mixed-effects modeling is carried out to find differences in pain and no pain states, considering subjects' health condition. The main result of the present pilot study is that physiological reactions to painful physiotherapy do not differ between patients with chronic pain and the control group. This finding concludes that studies on TMJ disorder procedures may also be performed on healthy subjects.

A Novel Labeling Method of Physiological-based Pressure Pain Assessment Among Patients With and Without Chronic Low Back Pain

Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Empatica E4 Assessment of Child Physiological Measures of Listening Effort During Remote and In-Person Communication.

Telepractice is a growing service model that delivers aural rehabilitation to deaf and hard-of hearing children via telecommunications technology. Despite known benefits of telepractice, this delivery approach may increase patients' listening effort (LE) characterized as an allocation of cognitive resources toward an auditory task. The study tested techniques for collecting physiological measures of LE in normal-hearing (NH) children during remote (referred to as tele-) and in-person communication using the wearable Empatica E4 wristband. Participants were 10 children (age range: 9-12 years old) who came to two tele- and two in-person weekly sessions, order counterbalanced. During each session, the children heard a short passage read by the clinical provider, completed an auditory passage comprehension task, and self-rated their effort as a part of the larger study. Measures of electrodermal activity and blood volume pulse amplitude were collected from the child E4 wristband. No differences in child subjective, physiological measures of LE or passage comprehension scores were found between in-person sessions and telesessions. However, an effect of treatment duration on subjective and physiological measures of LE was identified. Children self-reported a significant increase in LE over time. However, their physiological measures demonstrated a trend indicating a decrease in LE. A significant association between subjective measures and the passage comprehension task was found suggesting that those children who reported more effort demonstrated a higher proportion of correct responses. The study demonstrated the feasibility of collection of physiological measures of LE in NH children during remote and in-person communication using the E4 wristband. The results suggest that measures of LE are multidimensional and may reflect different sources of, or cognitive responses to, increased listening demand. https://doi.org/10.23641/asha.27122064.

Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study

BackgroundAlterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome (MetSyn). Personalized low-glycemic diets (PLGD) have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR come with limitations, including cost and invasiveness, hindering widespread adoption in primary disease prevention. Our aim is to assess machine-learning algorithms for predicting individual PPGR using non-invasive wearable data, thereby circumventing the limitations associated with existing approaches. By identifying the most accurate model, we seek to provide a more accessible and efficient method for managing glucose metabolism. MethodsThis data-driven analysis utilized the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for ten days. Blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) model-convolutional neural network (CNN), lightweight transformer (LTF), long short-term memory with attention (LSTM-A), and Bi-directional LSTM (BiLSTM)-were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs. ResultsThe proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error (RMSE) of 13.42 mg/dL, an average mean absolute percentage error (MAPE) of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis (CEGA), incorporating the leave-one-out cross-validation (LOOCV) strategy for a five-minute prediction horizon. ConclusionThe findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it emphasizes the promising prospects of combining deep learning with wearable technologies to predict glucose levels in healthy individuals.

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition

Stress is a prevalent issue in modern society which affects a large percentage of the population. Stress has negative effects on human daily lives such as reduced memory and concentration levels, forgetfulness, restlessness, and weakness. Although it is challenging to extinguish stress from human lives, monitoring and controlling its consequences can be implemented. Current methods for stress monitoring often lack interpretability of results and making it difficult to provide an interpretable system for immediate mental health counseling and intervention. Wearable devices are of growing interest due to the impact of the COVID-19 pandemic on mental health, as wearables provide monitoring of physiological parameters which helps in measuring stress levels. Here, an investigation on the feasibility of using wrist and chest-based multimodal physiological signals collected from wearables is executed for stress classification and identification of underlying modalities that impact the stress. Multimodalities from wrist and chest sensors are used to examine the three-class classification, baseline, stress and amusement. For improved and visualized effects, the multimodal signals from wearables were transformed into images. Then, a hybrid model was introduced, which comprised of attentive convolution neural network (CNN), transposed attentive CNN connections with long-short term memory (LSTM) and followed by attention layer for efficient feature extraction and improved classification accuracy. Results found that the proposed model with images provided 97 % accuracy and 96 % F1-score for chest wearable and 94 % accuracy and 93 % F1-score for wrist wearable data. Additionally, the post-hoc explainability approach (Local interpretable model agnostic applications, LIME) provided visual representations of contributing features from each signal for each class. LIME shows electrodermal activity (EDA), temperature (TEMP) and respiration (RESP) to be the significant factor in stress recognition from chest wearable and blood volume pulse (BVP) and EDA from wrist wearable. Additionally, increasing the number of features of the explainable model influences the modalities influence on the model explainability. The results establish a benchmark for explainable stress recognition using different sensor data.

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.

Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Recruitment is ongoing. This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features.

Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.

EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

The EmoWear dataset provides a bridge to explore Emotion Recognition (ER) via Seismocardiography (SCG), the measurement of small cardio-respiratory induced vibrations on the chest wall through Inertial Measurement Units (IMUs). We recorded Accelerometer (ACC), Gyroscope (GYRO), Electrocardiography (ECG), Blood Volume Pulse (BVP), Respiration (RSP), Electrodermal Activity (EDA), and Skin Temperature (SKT) data from 49 participants who watched validated emotionally stimulating video clips. They self-assessed their emotional valence, arousal, and dominance, as well as extra questions about the video clips. Also, we asked the participants to walk, talk, and drink, so that researchers can detect gait, voice, and swallowing using the same IMU. We demonstrate the effectiveness of emotion stimulation with statistical methods and verify the quality of the collected signals through signal-to-noise ratio and correlation analysis. EmoWear can be used for ER via SCG, ER during gait, multi-modal ER, and the study of IMUs for context-awareness. Targeted contextual information include emotions, gait, voice activity, and drinking, all having the potential to be sensed via a single IMU.

Emotion classification with multi‐modal physiological signals using multi‐attention‐based neural network

AbstractThe ability to effectively classify human emotion states is critically important for human‐computer or human‐robot interactions. However, emotion classification with physiological signals is still a challenging problem due to the diversity of emotion expression and the characteristic differences in different modal signals. A novel learning‐based network architecture is presented that can exploit four‐modal physiological signals, electrocardiogram, electrodermal activity, electromyography, and blood volume pulse, and make a classification of emotion states. It features two kinds of attention modules, feature‐level, and semantic‐level, which drive the network to focus on the information‐rich features by mimicking the human attention mechanism. The feature‐level attention module encodes the rich information of each physiological signal. While the semantic‐level attention module captures the semantic dependencies among modals. The performance of the designed network is evaluated with the open‐source Wearable Stress and Affect Detection dataset. The developed emotion classification system achieves an accuracy of 83.88%. Results demonstrated that the proposed network could effectively process four‐modal physiological signals and achieve high accuracy of emotion classification.

Autoencoder-based Photoplethysmography (PPG) signal reliability enhancement in construction health monitoring

Prior research has validated Photoplethysmography (PPG) as a promising biomarker for assessing stress factors in construction workers, including physical fatigue, mental stress, and heat stress. However, the reliability of PPG as a stress biomarker in construction workers is hindered by motion artifacts (MA) - distortions in blood volume pulse measurements caused by sensor movement. This paper develops a deep convolutional autoencoder-based framework, trained to detect and reduce MA in MA-contaminated PPG signals. The framework's performance is evaluated using PPG signals acquired from individuals engaged in specific construction tasks. The results demonstrate the framework has effectiveness in both detecting and reducing MA in PPG signals with a detection accuracy of 93% and improvement in signal-to-noise ratio by over 88%. This research contributes to a more reliable and error-reduced usage of PPG signals for health monitoring in the construction industry.

A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).

Evaluation of transformation invariant loss function with distance equilibrium in prediction of imaging photoplethysmography characteristics

Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative. Approach. In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC). Main results. The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively. Significance. This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.

Evaluation of a manualised neurofeedback training in psychosomatic-psychotherapeutic outpatient treatment (Neuro-pp-out): study protocol for a clinical mixed-methods pilot study

IntroductionElectroencephalographic neurofeedback (NFB), as a non-invasive form of brainwave training, has been shown to be effective in the treatment of various mental health disorders. However, only few results regarding manualised...

Evaluation of physiological response and synchronisation errors during synchronous and pseudosynchronous stimulation trials

Rhythm perception and synchronisation is musical ability with neural basis defined as the ability to perceive rhythm in music and synchronise body movements with it. The study aimed to check the errors of synchronisation and physiological response as a reaction of the subjects to metrorhythmic stimuli of synchronous and pseudosynchronous stimulation (synchronisation with an externally controlled rhythm, but in reality controlled or produced tone by tapping) Nineteen subjects without diagnosed motor disorders participated in the study. Two tests were performed, where the electromyography signal and reaction time were recorded using the NORAXON system. In addition, physiological signals such as electrodermal activity and blood volume pulse were measured using the Empatica E4. Study 1 consisted of adapting the finger tapping test in pseudosynchrony with a given metrorhythmic stimulus with a selection of preferred, choices of decreasing and increasing tempo. Study 2 consisted of metrorhythmic synchronisation during the heel stomping test. Numerous correlations and statistically significant parameters were found between the response of the subjects with respect to their musical education, musical and sports activities. Most of the differentiating characteristics shown evidence of some group division in the undertaking of musical activities. The use of detailed analyses of synchronisation errors can contribute to the development of methods to improve the rehabilitation process of subjects with motor dysfunction, and this will contribute to the development of an expert system that considers personalised musical preferences.

Comparing multimodal physiological responses to social and physical pain in healthy participants.

Previous physiology-driven pain studies focused on examining the presence or intensity of physical pain. However, people experience various types of pain, including social pain, which induces negative mood; emotional distress; and neural activities associated with physical pain. In particular, comparison of autonomic nervous system (ANS) responses between social and physical pain in healthy adults has not been well demonstrated. We explored the ANS responses induced by two types of pain-social pain, associated with a loss of social ties; and physical pain, caused by a pressure cuff-based on multimodal physiological signals. Seventy-three healthy individuals (46 women; mean age = 20.67 ± 3.27 years) participated. Behavioral responses were assessed to determine their sensitivity to pain stimuli. Electrocardiogram, electrodermal activity, photoplethysmogram, respiration, and finger temperature (FT) were measured, and 12 features were extracted from these signals. Social pain induced increased heart rate (HR) and skin conductance (SC) and decreased blood volume pulse (BVP), pulse transit time (PTT), respiration rate (RR), and FT, suggesting a heterogeneous pattern of sympathetic-parasympathetic coactivation. Moreover, physical pain induced increased heart rate variability (HRV) and SC, decreased BVP and PTT, and resulted in no change in FT, indicating sympathetic-adrenal-medullary activation and peripheral vasoconstriction. These results suggest that changes in HR, HRV indices, RR, and FT can serve as markers for differentiating physiological responses to social and physical pain stimuli.

IBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

Remote photo-plethysmography (rPPG) has emerged as a non-intrusive and promising physiological sensing capability in human–computer interface (HCI) research, gradually extending its applications in health-monitoring and clinical care contexts. With advanced machine learning models, recent datasets collected in real-world conditions have gradually enhanced the performance of rPPG methods in recovering heart-rate and heart-rate-variability metrics. However, the signal quality of reference ground-truth PPG data in existing datasets is by and large neglected, while poor-quality references negatively influence models. Here, this work introduces a new imaging blood volume pulse (iBVP) dataset of synchronized RGB and thermal infrared videos with ground-truth PPG signals from ear with their high-resolution-signal-quality labels, for the first time. Participants perform rhythmic breathing, head-movement, and stress-inducing tasks, which help reflect real-world variations in psycho-physiological states. This work conducts dense (per sample) signal-quality assessment to discard noisy segments of ground-truth and corresponding video frames. We further present a novel end-to-end machine learning framework, iBVPNet, that features an efficient and effective spatio-temporal feature aggregation for the reliable estimation of BVP signals. Finally, this work examines the feasibility of extracting BVP signals from thermal video frames, which is under-explored. The iBVP dataset and source codes are publicly available for research use.

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

BackgroundContinuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.MethodsWe used the dataset collected from a 10-week single-arm clinical trial in older adults (50–70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors’ data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using “leave-one-subject-out” and “leave-one-interval-out” cross-validation.ResultsThe final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.ConclusionsOur findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Understanding the role of emotion in decision making process: using machine learning to analyze physiological responses to visual, auditory, and combined stimulation.

Emotions significantly shape decision-making, and targeted emotional elicitations represent an important factor in neuromarketing, where they impact advertising effectiveness by capturing potential customers' attention intricately associated with emotional triggers. Analyzing biometric parameters after stimulus exposure may help in understanding emotional states. This study investigates autonomic and central nervous system responses to emotional stimuli, including images, auditory cues, and their combination while recording physiological signals, namely the electrocardiogram, blood volume pulse, galvanic skin response, pupillometry, respiration, and the electroencephalogram. The primary goal of the proposed analysis is to compare emotional stimulation methods and to identify the most effective approach for distinct physiological patterns. A novel feature selection technique is applied to further optimize the separation of four emotional states. Basic machine learning approaches are used in order to discern emotions as elicited by different kinds of stimulation. Electroencephalographic signals, Galvanic skin response and cardio-respiratory coupling-derived features provided the most significant features in distinguishing the four emotional states. Further findings highlight how auditory stimuli play a crucial role in creating distinct physiological patterns that enhance classification within a four-class problem. When combining all three types of stimulation, a validation accuracy of 49% was achieved. The sound-only and the image-only phases resulted in 52% and 44% accuracy respectively, whereas the combined stimulation of images and sounds led to 51% accuracy. Isolated visual stimuli yield less distinct patterns, necessitating more signals for relatively inferior performance compared to other types of stimuli. This surprising significance arises from limited auditory exploration in emotional recognition literature, particularly contrasted with the pleathora of studies performed using visual stimulation. In marketing, auditory components might hold a more relevant potential to significantly influence consumer choices.