Physiological Measurement

Objective: photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (MSPTD) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the MSPTD algorithm for PPG beat detection, named MSPTDfast (v.2). Approach. five potential improvements to MSPTD were identified and evaluated on four datasets. MSPTDfast (v.2) was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a high F₁-score. After internal validation, MSPTDfast (v.2) was benchmarked against state-of-the-art beat detection algorithms on four additional datasets. Main results.MSPTDfast (v.2) incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validation MSPTDfast (v.2) was found to have an execution time of between approximately one-third and one-twentieth of MSPTD, and a comparable F₁-score. During benchmarking MSPTDfast (v.2) was found to have the highest F₁-score alongside MSPTD, and amongst one of the lowest execution times with only MSPTDfast (v.1), qppgfast and MMPD (v.2) achieving shorter execution times. Significance.MSPTDfast (v.2) is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.

Objective. Electrical impedance tomography (EIT) generates cross-sectional images through non-invasive impedance measurements from surface electrodes. While impedance above 200 kHz can reveal intracellular properties, most existing EIT images are published at frequencies below 200 kHz. When frequencies exceed 200 kHz, the accuracy of impedance measurements declines due to the distributed circuit parameters such as parasitic capacitance, on-resistance of switch and the series inductance, with a more significant impact on larger impedance. To overcome this limitation, this paper proposes an approach to enhance the precision of impedance measurement through self-identification of distributed parameter. Approach. Firstly, the distributed circuit parameters are identified via correction measurements of precision resistances in the frequency range from 5 kHz to 1 MHz; then, the circuit is accurately modeled; finally, transfer impedance measurements during imaging process are corrected using the established circuit model. Main results. The distributed circuit parameter self-identification method was verified through a goodness-of-fit test, confirming the consistency between the model's predicted values and the actual values of the component. The test results indicate that at 1 MHz, the relative residuals follow a right-skewed distribution with an average value of 0.08%, which demonstrates high model accuracy. At 1 MHz, the relative error after correction for the 499 Ω precision resistor measurement is reduced by 12.01%, and for the 56 pF precision capacitor in parallel with 249 Ω, the relative error after correction is 0.46%. Significance. The proposed method can extend the frequency range of EIT and other impedance technologies from below 200 kHz to up to 1 MHz, while ensuring good measurement accuracy.

Objective. Atrial fibrillation (AFib) is a common cardiac arrhythmia associated with high morbidity and mortality, making early detection and continuous monitoring essential to prevent complications like stroke. This study explores the potential of using a ballistocardiogram (BCG) based bed sensor for the detection of AFib. Approach. We conducted a comprehensive clinical study with night hospital recordings from 116 patients, divided into 72 training and 44 test subjects. The study employs established methods such as autocorrelation to identify AFib from BCG signals. Spot and continuous Holter ECG were used as reference methods for AFib detection against which BCG rhythm classifications were compared. Results. Our findings demonstrate the potential of BCG-based AFib detection, achieving 94% accuracy on the training set using a rule-based method. Furthermore, the machine learning model trained with the training set achieved an AUROC score of 97% on the test set. Significance. This innovative approach shows promise for accurate, non-invasive, and continuous monitoring of AFib, contributing to improved patient care and outcomes, particularly in the context of home-based or hospital settings.

Objective. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although fruitful achievements have been reported on risk stratification of hypertension, the potential use of ambulatory blood pressure monitoring data is not well investigated. Different from single measuring blood pressure data, long-term blood pressure monitoring data can provide more comprehensive dynamical blood pressure information. Therefore, this paper proposes an intelligent hypertension risk stratification method based on ambulatory blood pressure monitoring data and improved machine learning algorithms. Approach. A total of 262 patients with hypertension are enrolled at People's Hospital of Yangjiang, in which 93 subjects are with simple hypertension and 169 subjects have hypertension with complication. Time-domain features, frequency-domain features, nonlinear dynamics features and correlation features underlying time-varying ambulatory blood pressure monitoring data are extracted to obtain discriminative feature representations. Synthetic minority over-sampling algorithm is applied to solve the problem of data balancing. The particle swarm optimization combined with kernel extreme learning machine is employed for feature fusion and optimization. Main results. The proposed method can yield a diagnostic accuracy of 93.7%, 97.8%, and 98.4% under two-, five- and ten-fold cross-validation, which demonstrates hypertension risk stratification in an intuitive, quantizable manner using multi-dimensional feature representation and learning. Significance. The proposed method is expected to provide early warning for latent serious cardiovascular diseases before obvious symptoms are present.

Objective. Occult hemorrhage (OH) can emerge subtly post-trauma, especially when internal bleeding is not yet severe enough to result in noticeable hemodynamic changes or shock. Despite normal appearances of traditional vital signs like heart rate (HR) and blood pressure (BP), clinically significant OH may be present, posing a critical diagnostic challenge. Early detection of OH, before vital signs begin to deteriorate, is vital as delays in identifying such conditions are linked to poorer patient outcomes. We analyze the performance of poly-anatomic multivariate technologies—including electrical impedance tomography (EIT), near-infrared spectroscopy (NIRS), electrical impedance spectroscopy (EIS), plethysmography (Pleth), and ECG—in a porcine model of OH. The goal was to detect OH without the need to know the subject's pre-established normal baseline. Approach. Forty female swine were bled at slow rates to create an extended period of subclinical hemorrhage, during which the animals' HR and BP remained stable before hemodynamic deterioration. Continuous vital signs, Pleth, and continuous non-invasive data were recorded and analyzed with the objective of developing an improved means of detecting OH. This detection was set up as a supervised voting classification problem where the measurement of each technology (minimally transformed) was used to train a classifier. A soft majority voting classification technique was then used to detect the existence of OH. Main Results. When comparing the prediction performance of the most significant univariate technology (EIT) to that of a poly-anatomic multivariate approach, the latter achieved higher area-under-the-curve (AUC) values from receiver operating characteristic analyses in almost every observation interval duration. In particular, after 21 min of continuous observation, the best AUC of the multivariate approach was 0.98, while that of the univariate approach was 0.92. The best multivariate technologies, in descending order, appeared to be EIT on the thorax, NIRS on the abdomen, and EIS on the thorax. Significance. In this clinically relevant porcine model of clinically OH, multivariate non-invasive measurements may be superior to univariate ones in detecting OH. Advanced technologies such as EIT, NIRS, and EIS exhibit considerably greater potential to accurately predict OH than standard physiological measurements. From a practical standpoint, our approach would not require the medical device to have prior access to non-hemorrhage baseline data for each patient. Early detection of OH using these technologies could improve patient outcomes by allowing for timely intervention before vital signs begin to deteriorate.

Background. Sudden cardiac death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. It ranks prominently among the leading causes of global mortality, contributing to approximately 10% of deaths worldwide. The timely anticipation of SCD holds the promise of immediate life-saving interventions, such as cardiopulmonary resuscitation. However, recent strides in the realms of deep learning (DL), machine learning (ML), and artificial intelligence have ushered in fresh opportunities for the automation of SCD prediction using physiological signals. Researchers have devised numerous models to automatically predict SCD using a combination of diverse feature extraction techniques and classifiers. Methods: We conducted a thorough review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD. Traditionally, specialists utilize molecular biomarkers, symptoms, and 12-lead ECG recordings for SCD prediction. However, continuous patient monitoring by experts is impractical, and only a fraction of patients seeks help after experiencing symptoms. However, over the past two decades, ML techniques have emerged and evolved for this purpose. Importantly, since 2021, the studies we have scrutinized delve into a diverse array of ML and DL algorithms, encompassing K-nearest neighbors, support vector machines, decision trees, random forest, Naive Bayes, and convolutional neural networks as classifiers. Results. This literature review presents a comprehensive analysis of ML and DL models employed in predicting SCD. The analysis provided valuable information on the fundamental structure of cardiac fatalities, extracting relevant characteristics from electrocardiogram (ECG) and heart rate variability (HRV) signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction. Conclusions. In recent years, researchers have made substantial strides in the prediction of SCD by leveraging openly accessible databases such as the MIT-BIH SCD Holter and Normal Sinus Rhythm, which contains extensive 24 h recordings of SCD patients. These sophisticated methodologies have previously demonstrated the potential to achieve remarkable accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30–70 min. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their applicability in real-world scenarios. Furthermore, these models predominantly utilize ECG and HRV signals, often overlooking the potential contributions of other physiological signals. Developing real-time, clinically applicable models also represents a critical avenue for further exploration in this field.

Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Objective. Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management. Methods. A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals. Results. The search yielded 5875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method. Conclusion. There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness. Significance. This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.

The radial artery, one of the terminal branches of the forearm, is utilized for vascular access and in various non-invasive measurement method, providing crucial medical insights. Various sensor technologies have been developed, each suited to specific characterization requirements. The work presented in this paper is based on a systematic literature review of the main publications relating to this topic. Analysis of the forearm vascular system complex array of anatomical structures shows that the radial artery can be characterized by its size, position, elasticity, tissue evaluation, blood flow and blood composition. The survey of medical procedures for patient monitoring, diagnosis and pre-operative validation shows the use of measures for pulse wave, blood pressure, heart rate, skin temperature, tissue response,$\ldots$ By exploring sensor technologies used for artery characterization, we produce a synthesis of measurement principles, measured phenomena and measurement accuracy for capacitive, piezoresistive, bioimpedance, thermography, fiber optic based, piezoelectric and photoacoustic sensors. A comparative study is conducted for sensor technologies by considering the metrics of the information to be collected and the associated accuracy as well as the portability, the complexity of the processing, the cost and the mode of contact with the arm. Finally, a comprehensive framework is proposed to facilitate informed decisions in the development of medical devices tailored to specific characterization needs.

Progressive overload describes the gradual increase of stress placed on the body during exercise training, and is often quantified (i.e. in resistance training studies) through increases in total training volume (i.e. sets × repetitions × load) from the first to final week of the exercise training intervention. Within the literature, it has become increasingly common for authors to discuss skeletal muscle growth adaptations in the context of increases in total training volume (i.e. the magnitude progression in total training volume). The present manuscript discusses a physiological rationale for progressive overload and then explains why, in our opinion, quantifying the progression of total training volume within research investigations tells very little about muscle growth adaptations to resistance training. Our opinion is based on the following research findings: (1) a noncausal connection between increases in total training volume (i.e. progressively overloading the resistance exercise stimulus) and increases in skeletal muscle size; (2) similar changes in total training volume may not always produce similar increases in muscle size; and (3) the ability to exercise more and consequently amass larger increases in total training volume may not inherently produce more skeletal muscle growth. The methodology of quantifying changes in total training volume may therefore provide a means through which researchers can mathematically determine the total amount of external 'work' performed within a resistance training study. It may not, however, always explain muscle growth adaptations.

Objective. Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments. Approach. This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects. Main results. Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error (MAE) of 0.15 ± 0.04°C, a root mean square error (RMSE) of 0.17 ± 0.05°C, and a mean absolute percentage error (MAPE) of 0.40 ± 0.12%. Statistical analysis further confirms the model's superior predictive capability compared to traditional methods. Significance. The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.

Objective.&#xD;Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range. &#xD;&#xD;Approach.&#xD;Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 seconds. A flow meter was used as reference.&#xD;&#xD;Main results.&#xD;Furthermore, the PCA-autocorrelation method was compared with a similar single axis method. The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [-1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCAautocorrelation method were within ±2 bpm of the reference. A Pearson correlation indicated a very strong correlation with r = 0.99 (p<0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [-14.9; 22.3 bpm], and r = 0.44 (p<0.001). &#xD;&#xD;Significance.&#xD;The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.

Objective: Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device. Methods: We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease. Results: The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient of r = 0.73, a mean absolute percentage error of 23% and a bias of −0.08. Conclusions: These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy. Significance: The benefit of a mobile ECG-based solution for measuring FEV1 is that it would&#xD;require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.

Objective: Electrical impedance tomography (EIT) has shown the ability to provide clinically useful functional information on ventilation in humans and other land mammals. We are motivated to use EIT with sea mammals and human divers, since EIT could provide unique information on lung ventilation that can help address diver performance and safety, and veterinary and behavioural questions. However, in-water use of EIT is challenging, primarily because sea water is more conductive than the body. Approach: We first address this issue by modelling the in-water component and evaluating image reconstruction algorithms. Main Results: EIT is able to produce reasonable images if an outer insulating layer allows a water layer thickness < 2% of the body radius. We next describe the design of custom EIT belts with an outer neoprene insulator to minimize current leakage. We show example underwater EIT recordings in human and dolphin subjects. Significance: we demonstrate in-water EIT is feasible with appropriate techniques.&#xD;

Objective: photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (MSPTD) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the MSPTD algorithm for PPG beat detection, named MSPTDfast (v.2). Approach. five potential improvements to MSPTD were identified and evaluated on four datasets. MSPTDfast (v.2) was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a high F₁-score. After internal validation, MSPTDfast (v.2) was benchmarked against state-of-the-art beat detection algorithms on four additional datasets. Main results.MSPTDfast (v.2) incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validation MSPTDfast (v.2) was found to have an execution time of between approximately one-third and one-twentieth of MSPTD, and a comparable F₁-score. During benchmarking MSPTDfast (v.2) was found to have the highest F₁-score alongside MSPTD, and amongst one of the lowest execution times with only MSPTDfast (v.1), qppgfast and MMPD (v.2) achieving shorter execution times. Significance.MSPTDfast (v.2) is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.

Objective. Atrial fibrillation (AFib) is a common cardiac arrhythmia associated with high morbidity and mortality, making early detection and continuous monitoring essential to prevent complications like stroke. This study explores the potential of using a ballistocardiogram (BCG) based bed sensor for the detection of AFib. Approach. We conducted a comprehensive clinical study with night hospital recordings from 116 patients, divided into 72 training and 44 test subjects. The study employs established methods such as autocorrelation to identify AFib from BCG signals. Spot and continuous Holter ECG were used as reference methods for AFib detection against which BCG rhythm classifications were compared. Results. Our findings demonstrate the potential of BCG-based AFib detection, achieving 94% accuracy on the training set using a rule-based method. Furthermore, the machine learning model trained with the training set achieved an AUROC score of 97% on the test set. Significance. This innovative approach shows promise for accurate, non-invasive, and continuous monitoring of AFib, contributing to improved patient care and outcomes, particularly in the context of home-based or hospital settings.

Objective. Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments. Approach. This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects. Main results. Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error (MAE) of 0.15 ± 0.04°C, a root mean square error (RMSE) of 0.17 ± 0.05°C, and a mean absolute percentage error (MAPE) of 0.40 ± 0.12%. Statistical analysis further confirms the model's superior predictive capability compared to traditional methods. Significance. The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.

Objective.&#xD;Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range. &#xD;&#xD;Approach.&#xD;Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 seconds. A flow meter was used as reference.&#xD;&#xD;Main results.&#xD;Furthermore, the PCA-autocorrelation method was compared with a similar single axis method. The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [-1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCAautocorrelation method were within ±2 bpm of the reference. A Pearson correlation indicated a very strong correlation with r = 0.99 (p<0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [-14.9; 22.3 bpm], and r = 0.44 (p<0.001). &#xD;&#xD;Significance.&#xD;The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.

Objective: Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device. Methods: We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease. Results: The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient of r = 0.73, a mean absolute percentage error of 23% and a bias of −0.08. Conclusions: These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy. Significance: The benefit of a mobile ECG-based solution for measuring FEV1 is that it would&#xD;require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.

Objective. Occult hemorrhage (OH) can emerge subtly post-trauma, especially when internal bleeding is not yet severe enough to result in noticeable hemodynamic changes or shock. Despite normal appearances of traditional vital signs like heart rate (HR) and blood pressure (BP), clinically significant OH may be present, posing a critical diagnostic challenge. Early detection of OH, before vital signs begin to deteriorate, is vital as delays in identifying such conditions are linked to poorer patient outcomes. We analyze the performance of poly-anatomic multivariate technologies—including electrical impedance tomography (EIT), near-infrared spectroscopy (NIRS), electrical impedance spectroscopy (EIS), plethysmography (Pleth), and ECG—in a porcine model of OH. The goal was to detect OH without the need to know the subject's pre-established normal baseline. Approach. Forty female swine were bled at slow rates to create an extended period of subclinical hemorrhage, during which the animals' HR and BP remained stable before hemodynamic deterioration. Continuous vital signs, Pleth, and continuous non-invasive data were recorded and analyzed with the objective of developing an improved means of detecting OH. This detection was set up as a supervised voting classification problem where the measurement of each technology (minimally transformed) was used to train a classifier. A soft majority voting classification technique was then used to detect the existence of OH. Main Results. When comparing the prediction performance of the most significant univariate technology (EIT) to that of a poly-anatomic multivariate approach, the latter achieved higher area-under-the-curve (AUC) values from receiver operating characteristic analyses in almost every observation interval duration. In particular, after 21 min of continuous observation, the best AUC of the multivariate approach was 0.98, while that of the univariate approach was 0.92. The best multivariate technologies, in descending order, appeared to be EIT on the thorax, NIRS on the abdomen, and EIS on the thorax. Significance. In this clinically relevant porcine model of clinically OH, multivariate non-invasive measurements may be superior to univariate ones in detecting OH. Advanced technologies such as EIT, NIRS, and EIS exhibit considerably greater potential to accurately predict OH than standard physiological measurements. From a practical standpoint, our approach would not require the medical device to have prior access to non-hemorrhage baseline data for each patient. Early detection of OH using these technologies could improve patient outcomes by allowing for timely intervention before vital signs begin to deteriorate.

Objective. Electrodermal activity (EDA) is a marker of psychophysiological arousal and is usually a measure of the skin conductance which is associated with sweat gland activity. Recent studies have shown that it is possible to estimate the EDA using contactless video based methods. Approach. Sensor EDA signals (SenEDA) and videos of the the palm were recorded simultaneously from over 30 participants under various stimuli (audio, video, cognitive and physiological). The luminance information from the video data was used to track sweat gland activity on the skin surface and extract the contactless signal luminance based EDA (LumEDA). Main results. Comparison of the SenEDA and LumEDA signals showed a high positive correlation between the two as expected. Significance. Under suitable illumination, simple spatial filters can be used to track sweat gland activity which can then be used to estimate signals analogous to the EDA. Such video based methods also facilitate spatio-temporal analysis of EDA correlates over larger areas of the body.

Objective: Electrical impedance tomography (EIT) has shown the ability to provide clinically useful functional information on ventilation in humans and other land mammals. We are motivated to use EIT with sea mammals and human divers, since EIT could provide unique information on lung ventilation that can help address diver performance and safety, and veterinary and behavioural questions. However, in-water use of EIT is challenging, primarily because sea water is more conductive than the body. Approach: We first address this issue by modelling the in-water component and evaluating image reconstruction algorithms. Main Results: EIT is able to produce reasonable images if an outer insulating layer allows a water layer thickness < 2% of the body radius. We next describe the design of custom EIT belts with an outer neoprene insulator to minimize current leakage. We show example underwater EIT recordings in human and dolphin subjects. Significance: we demonstrate in-water EIT is feasible with appropriate techniques.&#xD;

Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction. Approach. A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets. Main results. The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm. Significance. The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70–115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Objective. Electrical impedance tomography (EIT) is a non-invasive technique used for lung imaging. A significant challenge in EIT is reconstructing images of deeper thoracic regions due to the low sensitivity of boundary voltages to internal conductivity variations. The current injection pattern is decisive as it influences the current path, boundary voltages, and their sensitivity to tissue changes. Approach. This study introduces a novel current injection pattern with radially placed electrodes excited in a rotating radial pattern. The effectiveness of the proposed pattern was investigated using a 3D computational model that mimics the human thorax, replicating its geometry and tissue electrical properties. To examine the detection of lung anomalies, models representing both healthy and unhealthy states, including cancer-like anomalies in three different positions, were developed. The new pattern was compared to common patterns—adjacent, skip 1, and opposite—using finite element analysis. The comparison focused on the current density within lung nodules and the sensitivity to changes in anomaly positions. Main results. Results showed that the new pattern achieved the maximum current density within anomalies compared to surrounding tissues, with peak values near the closest electrode pairs to the anomalies. Specifically, current density magnitudes reached $72.73 \cdot 10^{-9}\,\mathrm{A}\cdot \mathrm{m}$, $145.24 \cdot 10^{-9}\,\mathrm{A}\cdot \mathrm{m}$, and $26.43 \cdot 10^{-9}\,\mathrm{A}\cdot \mathrm{m}$ for the three different positions, respectively. Furthermore, the novel pattern's sensitivity to anomaly position changes surpassed the common patterns. Significance. These results demonstrate the efficiency of the proposed injection pattern in detecting lung anomalies compared to the common injection patterns.