Embedded neonatal respiration monitoring

Current neonatal monitoring methods are not very comfortable for the neonate. The sticky electrodes used to measure the heart and breathing rate, can cause skin irritations and skin lesions when being pulled off. Furthermore, all the wires create a barrier for parents to touch and interact with their child.The E-Nemo (Embedded Neonatal Monitoring) project intends to change the way in which (premature) neonates are monitored in the neonatal intensive care unit (NICU). The aim of E-Nemo is to create a patient support system that assures comfort for the neonate and provides a more friendly environment for parental bonding, whilst keeping the current quality of vital sign monitoring.This report concerns the work related to the monitoring of only one vitals sign, namely: respiration.The aim of the E-Nemo respiration monitoring project is to design and develop a neonatal respiration monitoring system using sensors embedded in a patient support system (e.g., a mattress).A key challenge of this system is achieving the same robustness and reliability as existing monitoring equipment for neonates.Before the respiration sensor can be moved from the chest of the neonate into the underlying support system some questions need to be answered. Such as: Where can we place this sensor? Is one sensor enough? Which type of sensor is most suitable?To answer these (and more) questions regarding the design of the neonatal respiration monitoring system, a clinical trial was conducted at the NICU of the Máxima Medical Centre in Veldhoven.During this trial firsthand knowledge on the position and movement of neonates in an incubator, and general NICU workflow issues was gained.The clinical trial has resulted in a list of design specifications for the neonatal respiration monitoring system and a better understanding of the workflow and possible measurement disturbances in a NICU.Furthermore, this project has successfully demonstrated the possibility of measuring the neonatal respiration signal without direct skin contact with the neonate. However, in order to achieve the quality and reliability needed for intensive care respiration monitoring more research is necessary.Measuring the deformation of the mattress is expected to be a better measure for the respiration movements, than the pressure changes underneath the mattress which were measured in this study.Furthermore, more research is needed to determine the accuracy that will be demanded of the system, as this research has demonstrated that the current gold standard (transthoracic impedance plethysmography) does not function continously either

Early indicators for adverse development of cardiovascular, renal and metabolic function in children born with low birth weight

Prematurity affects more than 10% of infants worldwide and is the main reason for neonatal mortality. Improvements in neonatal care have led to higher survival rates into adulthood. Adverse events during organogenesis and development, intra-or extrauterine, can increase the risk for chronic disease later in life. Developmental origins of health and disease is the epidemiologic research field linking early life events to related clinical phenotypes. In this thesis, we present 4 studies designed to follow up consequences of prematurity or low birth weight at term compared to term controls with normal birth weight in two different cohorts. The first cohort of children, studied at a mean age of 9.7 and again at 12.6 years (studies I-III), were born either very preterm (<32 weeks gestational age) or at term but small for gestational age. We studied kidney volume and function, the autonomous nervous system using heart rate variability and identified markers for insulin resistance. The second cohort of children, studied at a mean age of 7.7 years (study IV), were born extremely preterm (<28 weeks gestational age). We measured kidney volume and function and divided the group into those who developed and those who did not developed nephrocalcinosis during the neonatal period. We also studied blood pressure at the time of their visit, including 24-h ambulatory blood pressure measurements. Kidney volume or function was not significantly different between the three groups in study I. In study IV we found that children born extremely premature had smaller kidneys then children born at term, in particular the right sided kidney volume was significantly smaller compared to controls. Preterm born girls had smaller kidneys than full-term born girls (controls) but preterm born boys were not different to controls. Among preterm born children without nephrocalcinosis girls, had smaller kidney volumes than boys. Kidney function was normal and not affected by kidney volume. Paper II showed signs for insulin resistance in very preterm born children and children born small for gestational age. Preterm born children presented signs for hepatic insulin resistance while small for gestational age born children had a decreased peripheral insulin sensitivity. Both, very preterm and full-term small for gestational age born children had a generalized depression of heart rate variability compared to controls indicating an impaired function of the autonomous nervous system (study III). Office blood pressure as well as 24-hour ambulatory blood pressure were in the normal range for children born very or extremely preterm as well as for children born small for gestational age at term. Circadian blood pressure regulation was adversely affected in 50% of children born extremely preterm illustrated by the absence of normal day-to-night dipping in 24-hour ambulatory blood pressure measurements (study IV). In conclusion, children born preterm or full-term but small for gestational age showed several morphological or functional changes at early school age. The detected changes are indicating a possible development towards impaired kidney function, hypertension and the metabolic syndrome

Wearable sensors for respiration monitoring: a review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective