Vastasyntyneen ja imeväisikäisen vauvan unenaikaisen hengitys- ja syketaajuuden tarkkailu puettavalla liikeanturilla

Vastasyntyneelle ja imeväisikäiselle nukkuminen on elintärkeä toiminto, ja se on välttämätöntä aivoverkkojen kehitykselle. Tiedetään, että huono unenlaatu aiheuttaa pitkällä tähtäimellä muun muassa kasvun hidastumista ja käyttäytymisongelmia. Imeväisikäisillä melko yleisesti esiintyvät unihäiriöt, kuten yöheräily ja nukahtamisvaikeudet aiheuttavat merkittävää rasitusta ja huolta vanhemmille. Objektiivisen mittausmenetelmän puutteen vuoksi ei ole kuitenkaan voitu selvittää imeväisikäisen unen kehittymistä kotiolosuhteissa. Tässä tutkimuksessa tarkasteltiin puettaviin pöksyihin kiinnitetyn liikeanturin ja EKG-kangaselektrodien soveltuvuutta vastasyntyneiden ja imeväisikäisten vauvojen unenaikaisen hengityksen ja sykkeen tarkkailuun. Tutkimuksen ensimmäisessä vaiheessa päiväaikaisten uni-EEG-tutkimuksien yhteydessä verrattiin liikeanturin mittauskanavien rekisteröimiä mittauskäyriä pietsoanturilla varustettuun hengitysvyöhön. Saatujen tutkimustuloksien perusteella liikeanturin gyroskooppi osoittautui tarkimmaksi hengitystaajuutta mittaavaksi parametriksi, kun taas anturin välittämä EKG-signaali oli tulkintakelpoisin osin luotettavaa. Tutkimuksen toisessa vaiheessa vauvaperheille annettiin unipöksyt ja älypuhelimet kotiin arvioidaksemme yön yli kestävää kotikäyttöä. Tutkimustulokset viittaavat siihen, että eri unitilojen tunnistaminen hengityksen vaihtelusta olisi todennäköisesti mahdollista gyroskooppisignaalista. Vanhemmilta saadun palautteen perusteella unipöksyjä pidettiin käytännöllisinä ja helppokäyttöisinä. Tulevissa tutkimuksissa tulisi keskittyä liikeanturin validointiin kliinisesti hyväksyttyjen mittausparametrien avulla, jotta algoritmeja voisi opettaa tunnistamaan eri uni-valve rytmejä automaattisesti. Näin puettava liikeanturi voisi tarjota tietoa vauvan luonnollisen unirakenteen kehittymisestä pitkällä aikavälillä. Lisäksi anturin kliininen validointi voisi mahdollistaa imeväisikäisten kardiorespiratoristen ongelmien ja liikehäiriöiden diagnostisen lisätyökalun kehittämisen.Sleep is one of the most vital functions of newborns and infants, and it is essential for neuronal network development. Therefore, long-term sleep disturbances have been associated with growth delays and behavioral disorders. Commonly reported infant sleep disturbances, such as night awakenings and difficulties falling asleep, cause distress to parents. Yet, the development of infant sleep in the home environment has not been fully elucidated due to lack of objective measurement parameters. In the current study, we assessed the feasibility of a motion sensor, attached to wearable pants, and ECG textile electrodes to monitor sleep-related respiration and heart rate of newborns and infants. First, we compared signals recorded by the motion sensor’s measurement channels to the standard respiratory piezo effort belt’s signal during daytime EEG recordings. According to our results, the motion sensor’s gyroscope proved to measure respiratory rate most accurately, while the ECG signal transmitted by the sensor was reliable in interpretable sections. We then provided wearable garments and smartphones to families with infants to assess overnight home-use. Our results indicate that different sleep states could likely be identified based on respiration fluctuation visible in the gyroscope’s signals. Moreover, the wearable system was considered practical and easy to use by the parents. Future studies should focus on validating the sensor with clinically approved measures, in order to train the algorithms to automatically identify different sleep-wake states. By doing so, the wearable sensor could provide information on natural infant sleep structure development over long time periods. Additionally, clinical validation of the sensor may result in the development of a companion diagnostic tool for infant cardiorespiratory and movement disorders

Assessment of breathing parameters using an inertial measurement unit (IMU)-based system

Breathing frequency (f B ) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best f B estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot’s breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM’s performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions

A novel acquisition platform for long-term breathing frequency monitoring based on inertial measurement units

[EN] Continuous monitoring of breathing frequency (f(B)) could foster early prediction of adverse clinical effects and exacerbation of medical conditions. Current solutions are invasive or obtrusive and thus not suitable for prolonged monitoring outside the clinical setting. Previous studies demonstrated the feasibility of deriving f(B) by measuring inclination changes due to breathing using accelerometers or inertial measurement units (IMU). Nevertheless, few studies faced the problem of motion artifacts that limit the use of IMU-based systems for continuous monitoring. Moreover, few attempts have been made to move towards real portability and wearability of such devices. This paper proposes a wearable IMU-based device that communicates via Bluetooth with a smartphone, uploading data on a web server to allow remote monitoring. Two IMU units are placed on thorax and abdomen to record breathing-related movements, while a third IMU unit records body/trunk motion and is used as reference. The performance of the proposed system was evaluated in terms of long-acquisition-platform reliability showing good performances in terms of duration and data loss amount. The device was preliminarily tested in terms of accuracy in breathing temporal parameter measurement, in static condition, during postural changes, and during slight indoor activities showing favorable comparison against the reference methods (mean error breathing frequency < 5%). Graphical abstract Proof of concept of a wearable, wireless, modular respiratory Holter based on inertial measurement units (IMUS) for the continuous breathing pattern monitoring through the detection of chest wall breathing-related movements.The authors thank "Fondazione per la Ricerca Scientifica Termale grants" for the financial support and all the participants. 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