Electrocardiography monitoring system and method

Systems and methods for electrocardiography monitoring use multiple capacitive sensors in order to determine reliable measurements of electrophysiological information of a patient. Relative coupling strength and/or reliability is used to select dynamically which sensors to use in order to determine, in particular, an electrocardiogram of the patient.</p

A review of recent advances in data analytics for post-operative patient deterioration detection

Most deaths occurring due to a surgical intervention\u3cbr/\u3ehappen postoperatively rather than during surgery.\u3cbr/\u3eThe current standard of care in many hospitals cannot fully\u3cbr/\u3ecope with detecting and addressing post-surgical deterioration\u3cbr/\u3ein time. For millions of patients, this deterioration is\u3cbr/\u3eleft unnoticed, leading to increased mortality and morbidity.\u3cbr/\u3ePostoperative deterioration detection currently relies on\u3cbr/\u3egeneral scores that are not fully able to cater for the complex\u3cbr/\u3epost-operative physiology of surgical patients. In the last\u3cbr/\u3edecade however, advanced risk and warning scoring techniques\u3cbr/\u3ehave started to show encouraging results in terms\u3cbr/\u3eof using the large amount of data available peri-operatively\u3cbr/\u3eto improve postoperative deterioration detection. Relevant\u3cbr/\u3eliterature has been carefully surveyed to provide a summary\u3cbr/\u3eof the most promising approaches as well as how they have\u3cbr/\u3ebeen deployed in the perioperative domain. This work also\u3cbr/\u3eaims to highlight the opportunities that lie in personalizing\u3cbr/\u3ethe models developed for patient deterioration for these\u3cbr/\u3eparticular post-surgical patients and make the output more\u3cbr/\u3eactionable. The integration of pre- and intra-operative data,\u3cbr/\u3ee.g. comorbidities, vitals, lab data, and information about\u3cbr/\u3ethe procedure performed, in post-operative early warning\u3cbr/\u3ealgorithms would lead to more contextualized, personalized,\u3cbr/\u3eand adaptive patient modelling. This, combined with careful integration in the clinical workflow, would result in improved clinical decision support and better post-surgical care outcomes

Unobtrusive monitoring of neonatal brain temperature using a zero-heat-flux sensor matrix

\u3cp\u3eThe temperature of preterm neonates must be maintained within a narrow window to ensure their survival. Continuously measuring their core temperature provides an optimal means of monitoring their thermoregulation and their response to environmental changes. However, existing methods of measuring core temperature can be very obtrusive, such as rectal probes, or inaccurate/lagging, such as skin temperature sensors and spotchecks using tympanic temperature sensors. This study investigates an unobtrusive method of measuring brain temperature continuously using an embedded zero-heat-flux (ZHF) sensor matrix placed under the head of the neonate. The measured temperature profile is used to segment areas of motion and incorrect positioning, where the neonate's head is not above the sensors. We compare our measurements during low motion/stable periods to esophageal temperatures for 12 preterm neonates, measured for an average of 5 h per neonate. The method we propose shows good correlation with the reference temperature for most of the neonates. The unobtrusive embedding of the matrix in the neonate's environment poses no harm or disturbance to the care work-flow, while measuring core temperature. To address the effect of motion on the ZHF measurements in the current embodiment, we recommend a more ergonomic embedding ensuring the sensors are continuously placed under the neonate's head.\u3c/p\u3

Electrocardiography monitoring system and method

\u3cp\u3eSystems and methods for electrocardiography monitoring use multiple capacitive sensors in order to determine reliable measurements of electrophysiological information of a patient. Relative coupling strength and/or reliability is used to select dynamically which sensors to use in order to determine, in particular, an electrocardiogram of the patient.\u3c/p\u3

Unobtrusive sleep state measurements in preterm infants - A review

Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states change in length and organization, with these changes as important indicators of brain development. As a result, several methods have been deployed to distinguish between the different preterm infant sleep states, among which polysomnography (PSG) is the most frequently used. However, this method is limited by the use of adhesive electrodes or patches that are attached to the body by numerous cables that can disturb sleep. Given the importance of sleep, this review explores more unobtrusive methods that can identify sleep states without disturbing the infant. To this end, after a brief introduction to preterm sleep states, an analysis of the physiological characteristics associated with the different sleep states is provided and various methods of measuring these physiological characteristics are explored. Finally, the advantages and disadvantages of each of these methods are evaluated and recommendations for neonatal sleep monitoring proposed

Pattern discovery in critical alarms originating from neonates under intensive care

Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms lead to alarm fatigue, a well-recognized patient safety issue. While multiple approaches to reduce alarm fatigue have been explored, patterns in alarming and inter-alarm relationships, as they manifest in the clinical workspace, are largely a black-box and hamper research efforts towards reducing alarms. The aim of this study is to detect opportunities to safely reduce alarm pressure, by developing techniques to identify, capture and visualize patterns in alarms.\u3cbr/\u3e\u3cbr/\u3eNearly 500 000 critical medical alarms were acquired from a neonatal intensive care unit over a 20 month period. Heuristic techniques were developed to extract the inter-alarm relationships. These included identifying the presence of alarm clusters, patterns of transition from one alarm category to another, temporal associations amongst alarms and determination of prevalent sequences in which alarms manifest.\u3cbr/\u3e\u3cbr/\u3eDesaturation, bradycardia and apnea constituted 86% of all alarms and demonstrated distinctive periodic increases in the number of alarms that were synchronized with nursing care and enteral feeding. By inhibiting further alarms of a category for a short duration of time (30 s/60 s), non-actionable physiological alarms could be reduced by 20%. The patterns of transition from one alarm category to another and the time duration between such transitions revealed the presence of close temporal associations and multiparametric derangement. Examination of the prevalent alarm sequences reveals that while many sequences comprised of multiple alarms, nearly 65% of the sequences were isolated instances of alarms and are potentially irreducible.\u3cbr/\u3e\u3cbr/\u3ePatterns in alarming, as they manifest in the clinical workspace were identified and visualized. This information can be exploited to investigate strategies for reducing alarms