Aggressive or Moderate Fluid Resuscitation in Acute Pancreatitis.

BACKGROUND: Early aggressive hydration is widely recommended for the management of acute pancreatitis, but evidence for this practice is limited. METHODS: At 18 centers, we randomly assigned patients who presented with acute pancreatitis to receive goal-directed aggressive or moderate resuscitation with lactated Ringer's solution. Aggressive fluid resuscitation consisted of a bolus of 20 ml per kilogram of body weight, followed by 3 ml per kilogram per hour. Moderate fluid resuscitation consisted of a bolus of 10 ml per kilogram in patients with hypovolemia or no bolus in patients with normovolemia, followed by 1.5 ml per kilogram per hour in all patients in this group. Patients were assessed at 12, 24, 48, and 72 hours, and fluid resuscitation was adjusted according to the patient's clinical status. The primary outcome was the development of moderately severe or severe pancreatitis during the hospitalization. The main safety outcome was fluid overload. The planned sample size was 744, with a first planned interim analysis after the enrollment of 248 patients. RESULTS: A total of 249 patients were included in the interim analysis. The trial was halted owing to between-group differences in the safety outcomes without a significant difference in the incidence of moderately severe or severe pancreatitis (22.1% in the aggressive-resuscitation group and 17.3% in the moderate-resuscitation group; adjusted relative risk, 1.30; 95% confidence interval [CI], 0.78 to 2.18; P = 0.32). Fluid overload developed in 20.5% of the patients who received aggressive resuscitation and in 6.3% of those who received moderate resuscitation (adjusted relative risk, 2.85; 95% CI, 1.36 to 5.94, P = 0.004). The median duration of hospitalization was 6 days (interquartile range, 4 to 8) in the aggressive-resuscitation group and 5 days (interquartile range, 3 to 7) in the moderate-resuscitation group. CONCLUSIONS: In this randomized trial involving patients with acute pancreatitis, early aggressive fluid resuscitation resulted in a higher incidence of fluid overload without improvement in clinical outcomes. (Funded by Instituto de Salud Carlos III and others; WATERFALL ClinicalTrials.gov number, NCT04381169.).

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.

Cystic fibrosis with liver involvement in adults has a benign course. Results from a tertiary referral center cohort.

BACKGROUND: Cystic Fibrosis Liver Disease is a poorly understood entity, especially in adults, in terms of its real prevalence, natural history and diagnostic criteria, despite being the most important extrapulmonary cause of mortality. The aim was to evaluate the prevalence, characteristics and potential risk factors of liver disease in adults with cystic fibrosis, according to two diagnostic criteria accepted in the scientific literature. METHODS: Patients were recruited in a tertiary referral hospital, and laboratory, ultrasound, non-invasive liver fibrosis tests (AST to Platelet Ratio Index; Fibrosis-4 Index) and transient elastography (Fibroscan) were performed. The proportion of patients with liver disease according to the Debray and Koh criteria were evaluated. RESULTS: 95 patients were included, 48 (50.5%) females, with a mean age of 30.4 (28.6-32.2) years. According to the Debray criteria, 6 (6.3%) patients presented liver disease. According to the Koh criteria, prevalence increased up to 8.4%, being statistically different from the 25% value described in other published series (p = 0.005). Seven (7.5%) presented ultrasonographic chronic liver disease. Eleven (13%) presented liver fibrosis according to the APRI score; 95 (100%) had a normal FIB-4 value. Mean liver stiffness value was 4.4 (4.1-4.7) kPa. FEV1 (OR=0.16, p 0.05), meconium ileus (OR=14.16, p 0.002), platelets (Pearson coefficient -0.25, p 0.05) and younger age (Pearson coefficient -0.19, p 0.05) were risk factors. CONCLUSIONS: Prevalence and severity of liver disease in adult cystic fibrosis patients were lower than expected. Meconium ileus, platelets, age and respiratory function were confirmed as risk factors associated to cystic fibrosis liver disease.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors.

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study.

Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 seconds window length.

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.

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.

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system.

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO2), which is commonly assessed by heart rate (HR) in field studies. However, the VO2-HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg, while the HR-Flex model had -5.01 and 5.36 mL/min/kg and the branched model, -6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.

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.

Per-oral endoscopic myotomy (POEM): a new endoscopic treatment for achalasia.

BACKGROUND/AIMS: Per-oral endoscopic myotomy (POEM) is a new minimally invasive technique to treat achalasia. METHODS: We performed a review of the literature of POEM with a special focus on technical details and the results obtained with this technique in patients with achalasia and other esophageal motility disorders. RESULTS: Thousands of POEM procedures have been performed worldwide since its introduction in 2008. The procedure is based on the creation of a mucosal entry point in the proximal esophagus to reach the cardia through a submucosal tunnel and then perform a myotomy of the muscular layers of the cardia, esophagogastric junction and distal esophagus, as performed in a Heller myotomy. The clinical remission rate ranges from 82 to 100%. Although no randomized studies exist and available data are from single-center studies, no differences have been found between laparoscopic Heller myotomy (LHM) and POEM in terms of perioperative outcomes, short-term outcomes (12 months) and long-term outcomes (up to three years). Procedure time and length of hospital stay were lower for POEM. Post-POEM reflux is a concern, and controversial data have been reported compared to LHM. The technique is safe, with no reported deaths related to the procedure and an adverse event rate comparable to surgery. Potential complications include bleeding, perforation, aspiration and insufflation-related adverse events. Thus, this is a complex technique that needs specific training even in expert hands. The indication for this procedure is widening and other motor hypercontractil esophageal disorders have been treated by POEM with promising results. POEM can be performed in complicated situations such as in pediatric patients, sigmoid achalasia or after failure of previous treatments. CONCLUSIONS: POEM is an effective treatment for achalasia and is a promising tool for other motor esophageal disorders. It is a safe procedure but, due to its technical difficulty and possible associated complications, the procedure should be performed in referral centers by trained endoscopists.

Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones.

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

Adaptive software architecture based on confident HCI for the deployment of sensitive services in Smart Homes.

Smart spaces foster the development of natural and appropriate forms of human-computer interaction by taking advantage of home customization. The interaction potential of the Smart Home, which is a special type of smart space, is of particular interest in fields in which the acceptance of new technologies is limited and restrictive. The integration of smart home design patterns with sensitive solutions can increase user acceptance. In this paper, we present the main challenges that have been identified in the literature for the successful deployment of sensitive services (e.g., telemedicine and assistive services) in smart spaces and a software architecture that models the functionalities of a Smart Home platform that are required to maintain and support such sensitive services. This architecture emphasizes user interaction as a key concept to facilitate the acceptance of sensitive services by end-users and utilizes activity theory to support its innovative design. The application of activity theory to the architecture eases the handling of novel concepts, such as understanding of the system by patients at home or the affordability of assistive services. Finally, we provide a proof-of-concept implementation of the architecture and compare the results with other architectures from the literature.

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.

Wearable biomedical measurement systems for assessment of mental stress of combatants in real time.

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the "Coincidente" program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach.

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM). New users of PPIs and H2 blockers (H2Bs) with CKD (eGFR < 60) were identified using a new-user and active-comparator design. Process mining discovery is a technique that discovers patterns and sequences in events over time, making it suitable for studying longitudinal eGFR trajectories. We used this technique to construct eGFR trajectory models for both PPI and H2B users. Our analysis indicated that PPI users exhibited more complex and rapidly declining eGFR trajectories compared to H2B users, with a 75% increased risk (adjusted hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.49 to 2.06) of transitioning from moderate eGFR stage (G3) to more severe stages (G4 or G5). These findings suggest that PPI use is associated with an increased risk of CKD progression, demonstrating the utility of process mining for longitudinal analysis in epidemiology, leading to an improved understanding of disease progression.

Stroke damage detection using classification trees on electrical bioimpedance cerebral spectroscopy measurements.

After cancer and cardio-vascular disease, stroke is the third greatest cause of death worldwide. Given the limitations of the current imaging technologies used for stroke diagnosis, the need for portable non-invasive and less expensive diagnostic tools is crucial. Previous studies have suggested that electrical bioimpedance (EBI) measurements from the head might contain useful clinical information related to changes produced in the cerebral tissue after the onset of stroke. In this study, we recorded 720 EBI Spectroscopy (EBIS) measurements from two different head regions of 18 hemispheres of nine subjects. Three of these subjects had suffered a unilateral haemorrhagic stroke. A number of features based on structural and intrinsic frequency-dependent properties of the cerebral tissue were extracted. These features were then fed into a classification tree. The results show that a full classification of damaged and undamaged cerebral tissue was achieved after three hierarchical classification steps. Lastly, the performance of the classification tree was assessed using Leave-One-Out Cross Validation (LOO-CV). Despite the fact that the results of this study are limited to a small database, and the observations obtained must be verified further with a larger cohort of patients, these findings confirm that EBI measurements contain useful information for   assessing on the health of brain tissue after stroke and supports the hypothesis that classification features based on Cole parameters, spectral information and the geometry of EBIS measurements are useful to differentiate between healthy and stroke damaged brain tissue.

Sensorized garments and textrode-enabled measurement instrumentation for ambulatory assessment of the autonomic nervous system response in the ATREC project.

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.

Seamlessly integrated textile electrodes and conductive routing in a garment for electrostimulation: design, manufacturing and evaluation.

Electro-stimulation to alleviate spasticity, pain and to increase mobility has been used successfully for years. Usually, gelled electrodes are used for this. In a garment intended for repeated use such electrodes must be replaced. The Mollii-suit by the company Inerventions utilises dry conductive rubber electrodes. The electrodes work satisfactory, but the garment is cumbersome to fit on the body. In this paper we show that knitted dry electrodes can be used instead. The knitted electrodes present a lower friction against the skin and a garment is easily fitted to the body. The fabric is stretchable and provides a tight fit to the body ensuring electrical contact. We present three candidate textrodes and show how we choose the one with most favourable features for producing the garment. We validate the performance of the garment by measuring three electrical parameters: rise time (10-90%) of the applied voltage, net injected charge and the low frequency value of the skin-electrode impedance. It is concluded that the use of flat knitting intarsia technique can produce a garment with seamlessly integrated conductive leads and electrodes and that this garment delivers energy to the body as targeted and is beneficial from manufacturing and comfort perspectives.

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.