Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.</p

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.</p

Heart rate detection by Fitbit ChargeHR™: A validation study versus portable polysomnography

Consumer "Smartbands" can collect physiological parameters, such as heart rate (HR), continuously across the sleep-wake cycle. Nevertheless, the quality of HR data detected by such devices and their place in the research and clinical field is debatable, as they are rarely rigorously validated. The objective of the present study was to investigate the reliability of pulse photoplethysmographic detection by the Fitbit ChargeHR (FBCHR, Fitbit Inc.) in a natural setting of continuous recording across vigilance states. To fulfil this aim, concurrent portable polysomnographic (pPSG) and the Fitbit's photoplethysmographic data were collected from a group of 25 healthy young adults, for ≥12hr. The pPSG-derived HR was automatically computed and visually verified for each 1-min epoch, while the FBCHR HR measurements were downloaded from the application programming interface provided by the manufacturer. The FBCHR was generally accurate in estimating the HR, with a mean (SD) difference of -0.66(0.04)beats/min (bpm) versus the pPSG-derived HR reference, and an overall Pearson's correlation coefficient (r) of 0.93 (average per participant r=0.85±0.11), regardless of vigilance state. The correlation coefficients were larger during all sleep phases (rapid eye movement, r=0.9662; N1, r=0.9918; N2, r=0.9793; N3, r=0.9849) than in wakefulness (r=0.8432). Moreover, the correlation coefficient was lower for HRs of &gt;100bpm (r=0.374) than for HRs of &lt;100bpm (r=0.84). Consistently, Bland-Altman analysis supports the overall higher accuracy in the detection of HR during sleep. The relatively high accuracy of FBCHR pulse rate detection during sleep makes this device suitable for sleep-related research applications in healthy participants, under free-living conditions

Biosensors in occupational safety and health management: A narrative review

A sensor is a device used to gather information registered by some biological, physical or chemical change, and then convert the information into a measurable signal. The first biosensor prototype was conceived more than a century ago, in 1906, but a properly defined biosensor was only developed later in 1956. Some of them have reached the commercial stage and are routinely used in environmental and agricultural applications, and especially, in clinical laboratory and industrial analysis, mostly because it is an economical, simple and efficient instrument for the in situ detection of the bioavailability of a broad range of environmental pollutants. We propose a narrative review, that found 32 papers and aims to discuss the possible uses of biosensors, focusing on their use in the area of occupational safety and health (OSH)

Biosensors in occupational safety and health management : a narrative review

A sensor is a device used to gather information registered by some biological, physical or chemical change, and then convert the information into a measurable signal. The first biosensor prototype was conceived more than a century ago, in 1906, but a properly defined biosensor was only developed later in 1956. Some of them have reached the commercial stage and are routinely used in environmental and agricultural applications, and especially, in clinical laboratory and industrial analysis, mostly because it is an economical, simple and efficient instrument for the in situ detection of the bioavailability of a broad range of environmental pollutants. We propose a narrative review, that found 32 papers and aims to discuss the possible uses of biosensors, focusing on their use in the area of occupational safety and health (OSH)

Oblivious Inspection: On the Confrontation between System Security and Data Privacy at Domain Boundaries

In this work, we introduce the system boundary security vs. privacy dilemma, where border devices (e.g., firewall devices) require unencrypted data inspection to prevent data exfiltration or unauthorized data accesses, but unencrypted data inspection violates data privacy. To shortcut this problem, we present Oblivious Inspection, a novel approach based on garbled circuits to perform a stateful application-aware inspection of encrypted network traffic in a privacy-preserving way. We also showcase an inspection algorithm for Fast Healthcare Interoperability Resources (FHIR) standard compliant packets along with its performance results. The results point out the importance of the inspection function being aligned with the underlying garbled circuit protocol. In this line, mandatory encryption algorithms for TLS 1.3 have been analysed observing that packets encrypted using Chacha20 can be filtered up to 17 and 25 times faster compared with AES128-GCM and AES256-GCM, respectively. All together, this approach penalizes performance to align system security and data privacy, but it could be appropriate for those scenarios where this performance degradation can be justified by the sensibility of the involved data such as healthcare scenarios

History of concussion and lowered heart rate variability at rest beyond symptom recovery:a systematic review and meta-analysis

Introduction: Concussion is a growing concern in worldwide sporting culture. Heart rate variability (HRV) is closely tied with autonomic nervous system (ANS) deficits that arise from a concussion. The objective of this review was to determine if a history of concussion (HOC) can impact HRV values in the time-domain in individuals at rest. This review works to add to the literature surrounding HRV testing and if it can be used to check for brain vulnerabilities beyond the recovery of concussion symptoms. Materials and methods: The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. A computer based systematic review scanned articles dating from 1996 to June 2023 through PubMed, Cochrane Library, Google Scholar, and EMBASE databases. A risk of bias assessment was conducted using the ROBINS-E tool. The average difference in time between heartbeats (MeanNN), the standard deviation of the differences (SDNN), and the root mean squared of the successive intervals (RMSSD) were measured. Results: Six total studies were found that fit the inclusion criteria including a total of 242 participants (133 without HOC, 109 with HOC). The average age of the control group was 23.3 ± 8.2, while the average age of the history of TBI group was 25.4 ± 9.7, with no significant difference between the groups (p = 0.202). Four of the studies reported no significant difference in any of the three measures, while two of the studies reported significant difference for all three measures. The meta-analysis was conducted and found that MeanNN (p = 0.03) and RMSSD (p = 0.04) reached statistical significance, while SDNN did not (p = 0.11). Conclusion: The results of this meta-analysis showed significant difference in two of the three HRV time-domain parameters evaluated. It demonstrates that there can be lowered HRV values that expand beyond the recovery of symptoms, reflecting an extensive period of ANS susceptibility after a concussion. This may be an important variable in determining an athlete’s return to play (RTP). Lack of homogenous study populations and testing methods introduces potential for bias and confounding factors, such as gender or age. Future studies should focus on baseline tests to compare individuals to themselves rather than matched controls

Validity and Reliability of Three Commercially Available Smart Sports Bras during Treadmill Walking and Running

A variety of wearable technology devices report heart rate. Heart rate sensing smart bras are manufactured for females who participate in activity, however accuracy has not been determined. The purpose was to determine the validity of heart rate measures in three commercially available sports bras during walking and running. Twenty-four healthy females completed bouts of treadmill exercise. The Adidas Smart sports bra, Berlei sports bra, and Sensoria Fitness biometric sports bra were tested. Participant perception of each garment was obtained immediately after the participant divested the sports bra. The Adidas Smart sports bra was valid only during rest (Intraclass correlation Coefficient [ICC] = 0.79, mean absolute percentage error [MAPE] = 4.5%, Limits of Agreement [LoA]=−8 to 8). The Berlei sports bra was valid across all conditions (ICC = 0.99, MAPE = 0.66%, LoA = −19 to 19), and the Sensoria biometric bra was valid during rest and walking (ICC = 0.96, MAPE = 1.9%, LoA = −15 to 12). Perception of the smart sports bras was higher for the Adidas Smart sports bra and Sensoria Fitness sports bra, and lower for the Berlei sports bra. Sports bra manufacturers designing wearable technology garments must consider the ability of returning accurate biometric data while providing necessary function and comfort to females engaging in physical activity

Workability and productivity among CTL machine operators - associations with sleep, fitness, and shift work

Operational performance of fully mechanized cut-to-length (CTL) harvesting varies greatly due to the human factor i.e. the machine operator. This study investigated how CTL machine operators' workability index (WAI), personal lifestyle choices, seasons, and shift work affected operational performance. Research evaluated 14 volunteer CTL machine operators for a longitudinal study with continuous data collection of productivity, activity level, sleep, and follow-up on a workability index questionnaire and fitness test every three months over a year. The study analyzed the production of 152 745.5 m(3) of timber combined with self-tracking data. Operators' relative productivity (P-r) had an increasing trend whilst WAI increased, thus WAI seems to work well also for forestry applications. Physical fitness (VO2max) didn't seem to connect with P-r and WAI had only a slightly increasing trend when VO2max increased. The participants slept longer in the evening shift than in the morning shift (p < 0.000) consequently catching up on their sleep deficit from the morning shift period. Furthermore, operators' higher sleep value (SV) in the evening shift increased P-r in the final fellings. The results should be of interest to both practitioners and researchers interested in the productivity of harvesting operations

Measuring the effects of cognitive stress and relaxation using a wearable smart ring

Abstract. Prolonged stress is known to be a risk factor for various kinds of diseases, such as cardiovascular diseases. If stress could be easily measured, it would enable monitoring of stress and help people make better choices to achieve a healthier lifestyle. In this study, a polysomnography system as well as a wearable smart ring were used to measure the responses of central and autonomic nervous systems from ten healthy test subjects (five male and five female), aged 23–26. The responses were measured in two conditions: cognitive stress induced by a mental calculation task and relaxation induced by a focused attention meditation exercise. Power spectral densities of two electroencephalography frequency bands, alpha and beta, were calculated to represent the central nervous system response. The autonomic nervous system response was measured using heart rate, heart rate variability and peripheral (finger) temperature. In cognitive stress, alpha and beta bands both showed higher activity, increasing by 53.26% and 94.70%, respectively. Heart rate also increased by 19.33%, while heart rate variability decreased by 25.65% and peripheral temperature change was 0.77℃ lower. Results show that the changes in autonomic nervous system responses acquired by the smart ring correlate with the changes in central nervous system responses acquired by the polysomnography system. This suggests that a smart ring could be used for an indirect measurement of human stress level. Follow-up studies with larger sample sizes are needed to confirm the findings of this study and to determine the most suitable features for representation of human stress level.Kognitiivisen stressin ja rentoutumisen vaikutusten mittaaminen älysormuksella. Tiivistelmä. Pitkittynyt stressi toimii riskitekijänä lukuisille sairauksille, kuten sydän- ja verisuonitaudeille. Stressin vaivaton mittaaminen mahdollistaisi stressitason seuraamisen, mikä vuorostaan auttaisi ihmisiä tekemään parempia valintoja terveellisemmän elämäntyylin puolesta. Tässä tutkimuksessa käytettiin polysomnografialaitteistoa sekä puettavaa älysormusta keskushermoston ja autonomisen hermoston vasteiden mittaamiseen kymmeneltä terveeltä koehenkilöltä (viisi miestä ja viisi naista), iältään 23–26. Vasteet mitattiin kahdessa tilassa: päässälaskutehtävän aikaansaamassa kognitiivisessa stressissä sekä hengitykseen keskittyvän meditaatioharjoituksen aikaansaamassa rentoutumisessa. Kahdelle elektroenkefalografian taajuuskaistalle, alfalle ja beetalle, laskettiin tehon spektritiheydet kuvastamaan keskushermoston vastetta. Lisäksi laskettiin syke, sykevälivaihtelu sekä ääreislämpötila (sormen lämpötila) kuvastamaan autonomisen hermoston vastetta. Kognitiivisessa stressissä sekä alfa- että beetakaistan aktiivisuus kasvoi, alfalla 53,26 % ja beetalla 94,70 %. Myös syke nousi 19,33 %, kun taas sykevälivaihtelu pieneni 25,65 % ja ääreislämpötilan muutos oli 0,77 ℃ pienempi. Tulokset osoittavat, että älysormuksella mitatut autonomisen hermoston vasteen muutokset korreloivat polysomnografialaitteistolla mitattujen keskushermoston vasteen muutosten kanssa. Tämä antaa ymmärtää, että älysormusta voitaisiin käyttää ihmisen stressitason epäsuoraan mittaamiseen. Suuremman kokoluokan jatkotutkimuksia tarvitaan varmistamaan tämän tutkimuksen löydökset sekä määrittämään sopivimmat fysiologiset piirteet kuvastamaan ihmisen stressitasoa