The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals.

In modern hospitals, monitoring patients' vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.

Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

Process mining and data mining applications in the domain of chronic diseases: A systematic review.

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities.

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements.

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth-skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth-skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.

Measurements and observations of movements at work for warehouse forklift truck operators.

Inclinometry and video analyses can provide objective measures of physical workloads. The study aim was to measure and observe arm, back and head postures and movements among forklift truck operators (FLTOs) during a working day, analyzing differences between types of forklift trucks and to assess reported workload and health. Twenty-five male FLTOs in a high-level warehouse were randomly included. The data collected comprised technical measurements, video analyses of postures and movements, and a questionnaire measuring health, pain and workload. On average, the FLTOs rotated their head more than 45°, in total, 232 times/h. Video analysis revealed that FLTOs periodically drive the forklift truck sideways with the head rotated in the direction of travel, and in periods look upwards, in which the head is highly rotated and extended. Inclinometry and observations during the working day has the potential to be a valuable part of risk assessment promoting occupational safety and health.

Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback.

Vibrotactile feedback training may be one possible method for interventions that target at learning better work techniques and improving postures in manual handling. This study aimed to evaluate the short term effect of real-time vibrotactile feedback on postural exposure using a smart workwear system for work postures intervention in simulated industrial order picking. Fifteen workers at an industrial manufacturing plant performed order-picking tasks, in which the vibrotactile feedback was used for postural training at work. The system recorded the trunk and upper arm postures. Questionnaires and semi-structured interviews were conducted about the users' experience of the system. The results showed reduced time in trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30° and ≥45° when the workers received feedback, and for trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30°, after feedback withdrawal. The workers perceived the system as useable, comfortable, and supportive for learning. The system has the potential of contributing to improved postures in order picking through an automated short-term training program.

Textile-Friendly Interconnection between Wearable Measurement Instrumentation and Sensorized Garments-Initial Performance Evaluation for Electrocardiogram Recordings.

The interconnection between hard electronics and soft textiles remains a noteworthy challenge in regard to the mass production of textile-electronic integrated products such as sensorized garments. The current solutions for this challenge usually have problems with size, flexibility, cost, or complexity of assembly. In this paper, we present a solution with a stretchable and conductive carbon nanotube (CNT)-based paste for screen printing on a textile substrate to produce interconnectors between electronic instrumentation and a sensorized garment. The prototype connectors were evaluated via electrocardiogram (ECG) recordings using a sensorized textile with integrated textile electrodes. The ECG recordings obtained using the connectors were evaluated for signal quality and heart rate detection performance in comparison to ECG recordings obtained with standard pre-gelled Ag/AgCl electrodes and direct cable connection to the ECG amplifier. The results suggest that the ECG recordings obtained with the CNT paste connector are of equivalent quality to those recorded using a silver paste connector or a direct cable and are suitable for the purpose of heart rate detection.

The cholinergic anti-inflammatory pathway in resistant hypertension treated with renal denervation.

BACKGROUND: Renal denervation (RDN) reduces sympathetic tone and may alter the sympathetic-parasympathetic balance. The autonomic nervous system is partly a regulator of innate immunity via the cholinergic anti-inflammatory pathway (CAP) which inhibits inflammation via the vagus nerve. Placental Growth Factor (PlGF) influences a neuro-immunological pathway in the spleen which may contribute to hypertension. The aim of this study was to investigate if modulation of renal sympathetic nerve activity affects CAP in terms of cytokine release as well as levels of PlGF. METHODS: Ten patients treated with RDN (Medtronic Inc), were analyzed for TNF, IL-1b and IL-10 and Lipopolysaccharide (LPS)-stimulated cytokine release before RDN, 1 day after and at 3- and 6-months follow-up. Four patients who underwent elective coronary angiography served as disease controls (DC). RESULTS: Baseline TNF was significantly lower 1 day after RDN (p = 0.03). LPS-stimulated (0, 10 and 100 ng/mL) TNF and IL-1b were significantly lower 1 day after RDN (TNF p = 0.0009, p = 0.0009 and p = 0.001, IL-1b; p = 0.0001, p = 0.002 and p = 0.005). IL-10 was significantly higher one day after RDN (p = ns, p = 0.02 and p = 0.01). These differences however declined during follow up. A more marked TNF reduction was achieved with a cholinergic analogue, GTS-21, in LPS-stimulated whole blood as compared with samples without GTS-21. Cytokine levels in controls did not differ before and 1 day after coronary angiography. PlGF was significantly higher in RDN patients and DC compared with healthy controls but did not change during follow-up. CONCLUSION: RDN has an immediate effect on TNF in vivo and cytokine release ex vivo but seems to wane over time suggesting that current RDN techniques may not have long-lasting immunomodulatory effect. Repeated and extended stimulation of CAP in resistant hypertension by targeting neural circuits may be a potential therapeutic strategy for treatment of both hypertension and inflammation.

P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing.

Preventive healthcare has attracted much attention recently. Improving people's lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals' work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.

Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation.

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21⁻65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R² = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R² = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R² = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

Full Impedance Cardiography measurement device using Raspberry PI3 and System-on-Chip biomedical Instrumentation Solutions.

Impedance Cardiography (ICG) is a non-invasive method for monitoring cardiac dynamics using Electrical Bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper present a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full 3-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recording were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents, will be available on www.BiosignalPI.com, for open access under a Non Commercial-Share A like 4.0 International License.

Image enhancement effect on inter and intra-observer reliability of nailfold capillary assessment.

BACKGROUND: Nailfold capillaroscopy (NC) is a diagnostic imaging technique that is used to assess the blood capillary network in the nailfold area. NC is routinely used for patients with microcirculation problems, such as systemic sclerosis and other connective tissue diseases. Experts commonly use subjective evaluation as a reference point in images of nailfold video capillaroscopy, so it is important to reduce the inherent ambiguities in human judgment and diagnosis. Image quality is an important factor that affects measurement error and assessment time of NC images. OBJECTIVE: In this study, a new image enhancement technique was introduced and evaluated subjectively. METHODS: In total, 475 nailfold video capillaroscopy images from 18 healthy subjects and 41 systemic lupus erythematosus patients were used. The images were randomly divided into two sets, one each with 275 and 200. Eight independent observers who were familiar with the capillaroscopy technique participated in this study. The set of 275 images was evaluated by three observers with the forced-choice pairwise comparison method. Elliptic broken line (EBL) was used to count the number of capillaries. The intra- and inter-observer reliability of the original and enhanced images was evaluated on 200 images by five observers. RESULT: Except for eight images, all observers preferred the enhanced images in the visual quality comparison method. The intra-class correlation coefficient (ICC) of intra- and inter-observer reliability increased from 0.76-0.84 to 0.82-0.89, respectively, when using the enhancement method. CONCLUSION: By improving the image quality, more capillary details will be visible, and an observer can document more details that may not be visible in the original image and can do so more efficiently.

Validation of a computerized algorithm to quantify fetal heart rate deceleration area.

INTRODUCTION: Reliability in visual cardiotocography interpretation is unsatisfying, which has led to the development of computerized cardiotocography. Computerized analysis is well established for antenatal fetal surveillance but has yet not performed sufficiently during labor. We aimed to investigate the capacity of a new computerized algorithm compared with visual assessment in identifying intrapartum fetal heart rate baseline and decelerations. MATERIAL AND METHODS: In all, 312 intrapartum cardiotocography tracings with variable decelerations were analyzed by the computerized algorithm and visually examined by two observers, blinded to each other and the computer analysis. The width, depth and area of each deceleration was measured. Four cases (>100 variable decelerations) were subjected to in-depth detailed analysis. The outcome measures were bias in seconds (width), beats per minute (depth), and beats (area) between computer and observers using Bland-Altman analysis. Interobserver reliability was determined by calculating intraclass correlation and Spearman rank analysis. RESULTS: The analysis (312 cases) showed excellent intraclass correlation (0.89-0.95) and very strong Spearman correlation (0.82-0.91). The detailed analysis of >100 decelerations in four cases revealed low bias between the computer and the two observers; width 1.4 and 1.4 seconds, depth 5.1 and 0.7 beats per minute, and area 0.1 and -1.7 beats. This was comparable to the bias between the two observers: 0.3 seconds (width), 4.4 beats per minute (depth) and 1.7 beats (area). The intraclass correlation was excellent (0.90-.98). CONCLUSION: A novel computerized algorithm for intrapartum cardiotocography analysis is as accurate as gold standard visual assessment, with high correlation and low bias.

Elliptical broken line method for calculating capillary density in nailfold capillaroscopy: Proposal and evaluation.

Nailfold capillaroscopy is a practical method for identifying and obtaining morphological changes in capillaries which might reveal relevant information about diseases and health. Capillaroscopy is harmless, and seems simple and repeatable. However, there is lack of established guidelines and instructions for acquisition as well as the interpretation of the obtained images; which might lead to various ambiguities. In addition, assessment and interpretation of the acquired images are very subjective. In an attempt to overcome some of these problems, in this study a new modified technique for assessment of nailfold capillary density is introduced. The new method is named elliptic broken line (EBL) which is an extension of the two previously known methods by defining clear criteria for finding the apex of capillaries in different scenarios by using a fitted elliptic. A graphical user interface (GUI) is developed for pre-processing, manual assessment of capillary apexes and automatic correction of selected apexes based on 90° rule. Intra- and inter-observer reliability of EBL and corrected EBL is evaluated in this study. Four independent observers familiar with capillaroscopy performed the assessment for 200 nailfold videocapillaroscopy images, form healthy subject and systemic lupus erythematosus patients, in two different sessions. The results show elevation from moderate (ICC=0.691) and good (ICC=0.753) agreements to good (ICC=0.750) and good (ICC=0.801) for intra- and inter-observer reliability after automatic correction of EBL. This clearly shows the potential of this method to improve the reliability and repeatability of assessment which motivates us for further development of automatic tool for EBL method.

Cholinergic anti-inflammatory pathway activity in dialysis patients: a role for neuroimmunomodulation?

BACKGROUND: The cholinergic anti-inflammatory pathway (CAP) modulates inflammatory responses through the vagus nerve and the α-7-nicotinic acetylcholine receptor (α7nAChR) on macrophages and immune cells. Sympathetic/parasympathetic imbalance and chronic inflammation are both linked to poor outcome in dialysis patients. The aim of this study was to investigate CAP activity in these patients. METHODS: Twenty dialysis patients, 12 hemodialysis (HD) and 8 peritoneal dialysis (PD) patients (12 male, 8 female; age range 47-83 years) and 8 controls (5 male, 3 female; age range 31-52 years) were analyzed for C-reactive protein (CRP), tumor necrosis factor (TNF), interleukin-1b (IL-1b), IL-6 and IL-10 at baseline. The cytokines were then assessed after whole blood stimulation ex vivo with lipopolysaccharide (LPS) (10 and 100 ng/mL) and again in the presence of 45 and 90 µmol/L GTS-21, a cholinergic α7nAChR agonist. RESULTS: CRP, TNF, IL-1 and IL-6 were significantly higher, whereas IL-10 was significantly lower at baseline in patients compared with controls. After LPS stimulation, TNF increased significantly more in patients than in controls but decreased to similar levels in both groups after addition of GTS-21. IL-6 attenuation was comparable with TNF and the IL-1b pattern was similar but remained significantly higher in patients. Interestingly, IL-10 increased after GTS-21 in a dose-dependent manner, but only in patients. Results in HD and PD patients did not differ. CONCLUSIONS: The response of immune cells after LPS exposure and cholinergic stimulation suggests a functional CAP in dialysis patients. It may thus be possible to target the α7nAChR control of cytokine release as an anti-inflammatory strategy and thereby improve outcome in these patients.

Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations.

For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications.

Biosignal PI, an affordable open-source ECG and respiration measurement system.

Bioimedical pilot projects e.g., telemedicine, homecare, animal and human trials usually involve several physiological measurements. Technical development of these projects is time consuming and in particular costly. A versatile but affordable biosignal measurement platform can help to reduce time and risk while keeping the focus on the important goal and making an efficient use of resources. In this work, an affordable and open source platform for development of physiological signals is proposed. As a first step an 8-12 leads electrocardiogram (ECG) and respiration monitoring system is developed. Chips based on iCoupler technology have been used to achieve electrical isolation as required by IEC 60601 for patient safety. The result shows the potential of this platform as a base for prototyping compact, affordable, and medically safe measurement systems. Further work involves both hardware and software development to develop modules. These modules may require development of front-ends for other biosignals or just collect data wirelessly from different devices e.g., blood pressure, weight, bioimpedance spectrum, blood glucose, e.g., through Bluetooth. All design and development documents, files and source codes will be available for non-commercial use through project website, BiosignalPI.org.

Automated NREM sleep staging using the electro-oculogram: a pilot study.

Automatic sleep staging from convenient and unobtrusive sensors has received considerable attention lately because this can enable a large range of potential applications in the clinical and consumer fields. In this paper the focus is on achieving non-REM (NREM) sleep staging from ocular electrodes. From these signals, specific patterns related to sleep such as slow eye movements, K-complexes, eye blinks, and spectral features are estimated. Although such patterns are characteristic of the Electroencephalogram, they can also be visible to a lesser extent on signals from ocular electrodes. Automatic sleep staging was implemented using two approaches: i) based on a state-machine and ii) using a neural network. The first one relied on the recommendations of the American Academy of Sleep Medicine, and the second one used a multilayer perceptron which was trained on manually sleep-staged data. Results were obtained on the data of five volunteers who participated in a nap experiment. Manual sleep staging of this data, performed by an expert, was used as reference. Five stages were considered, namely wake with eyes open, wake with eyes closed, and sleep stages N1, N2, and N3. The results were characterized in terms of confusion matrices from which the Cohen's κ coefficients were estimated. The values of κ for both the state-machine and neural-network based automatic sleep staging approaches were 0.79 and 0.59 respectively. Thus, the state-machine based approach shows a very good agreement with manual staging of sleep-data.