Learning medical alarms whilst performing other tasks.

Two studies are reported which first observe, and then attempt to replicate, the cognitive demands of intensive care unit (ICU) activity whilst concurrently learning audible alarms. The first study, an observational study in an ICU ward, showed that the alarms are very frequent and co-occur with some activities more than others. The three most frequently observed activities observed in the ICU were drugs (calculation, preparation and administration), patient observation and talking. The cognitive demands of these activities were simulated in a second, laboratory-based experiment in which alarms were learned. The results showed that performance in the alarm task generally improved as participants were exposed to more repetitions of those alarms, but that performance decrements were observed in the secondary tasks, particularly when there were two or three of them. Some confusions between the alarms persisted to the end of the study despite prolonged exposure to the alarms, confusions which were likely caused by both acoustic and verbal labelling similarities. PRACTITIONER SUMMARY: The cognitive demands of working in an ICU were observed and simulated whilst alarms were learned. Alarms should generally avoid sharing similar rhythmic (and other) characteristics. The simulation task described here could be used for testing alarm learning without requiring a clinical environment

Sleep-wake stages classification using heart rate signals from pulse oximetry

The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.Fil: Casal, Ramiro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; ArgentinaFil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentin

Cuffless ambulatory blood pressure measurement using the photoplethysmogram and the electrocardiogram

Blood pressure (BP), as with other vital signs such as heart rate and respiratory rate, exhibits endogenous oscillations over a period of approximately 24 hours, a phenomenon known as circadian rhythmicity. This rhythm typically reaches a nadir during sleep, however, different BP circadian rhythm phenotypes exist depending on the magnitude and direction of the nocturnal change. Analysis of these phenotypes has been shown to be an independent indicator for the onset of cardiovascular disease, the leading cause of non-communicable mortality and morbidity worldwide. However, currently the established technique for monitoring BP over 24 hours in the general population requires an inflatable cuff wrapped around the upper arm. This procedure is highly disruptive to sleep and daily life, and therefore rarely performed in primary care. Although commercial cuffless BP devices do exist, their accuracy has been questioned, and consequently, the clinical community do not recommend their use. In this thesis, I investigated techniques to measure BP in an ambulatory environment without an inflatable cuff using two signals commonly acquired by wearable sensors: the photoplethysmogram (PPG) and the electrocardiogram (ECG). Given the diverse mechanisms by which the autonomic nervous system regulates BP, I developed methodologies using data from multiple individuals with BP perturbed by various, diverse, mechanisms. To identify surrogate measures of BP derived from the PPG and ECG signals, I designed a clinical study in which significant BP changes were induced through a pharmacological intervention in thirty healthy volunteers. Using data from this study, I established that changes in the pulse arrival time (PAT, the time delay between fiducial points on the ECG and PPG waveforms) and morphological features of the PPG waveform could provide reliable cuffless indicators for changes in BP. Even at rest, however, these signals are confounded by factors such as the pre-ejection period (PEP) and signal measurement noise. Additionally, accurate absolute measurements of BP required calibration using a reference BP device. Subsequently, I conducted a circadian analysis of these surrogate measures of BP using a large cohort of 1,508 patients during the 24-hour period prior to their discharge from an intensive care unit. Through this circadian analysis I suggest that PAT and a subset of features from the PPG waveform exhibit a phenotypically modified circadian rhythm in synchronicity with that of BP. Additionally, I designed a novel ordinal classification algorithm, which utilised circadian features of these signals, in order to identify BP circadian rhythm profiles in a calibration-free manner. This method may provide a cost-effective initial assessment of BP phenotypes in the general population. Notably, estimating absolute BP values using PPG and ECG signals in the ICU resulted in clinically significant mean absolute errors of 9.26 (5.01) mmHg. Finally, I designed a clinical study to extend the work towards cuffless ambulatory BP estimation in a cohort of fifteen healthy volunteers. Hybrid calibration strategies (where model personalisation was handled by user demographics, commonly utilised by commercial cuffless devices) led to clinically significant errors when estimating absolute values of BP, mean absolute error = 9.62 (19.73) mmHg. For the majority of individuals, a more appropriate estimation of BP values was achieved through an individual calibration strategy whereby idiosyncratic models were trained on personalised data, mean absolute error = 5.45 (6.40) mmHg. However, for a handful of individuals, notable estimation errors (>10 mmHg) still persisted using this strategy largely as a result of motion artifacts, inherent intra- and inter-individual variability in PPG features, and inadequate training data. Overall, I suggest that while beat-by-beat measurements of BP can be obtained using PPG and ECG signals, their accuracy is significantly limited in an ambulatory environment. This limitation, combined with the impracticality of individual calibration (due to the low tolerance for ABPM), suggest that cuffless ambulatory blood pressure measurement using the PPG and ECG signals may be infeasible. Nevertheless, macro assessments of cardiovascular health, such as an individual's BP phenotype, may be comparatively more accurately predicted using these signals with the potential to be recorded without calibration. Through further research on the relationship between the circadian rhythms of BP and the PPG and ECG waveforms, it is promising that these signals may be able to assist in detecting deterioration in cardiovascular health in the general population

Physiological Investigation of Localized Temperature Effects on Vigilance Performance

Despite a long history of vigilance research, the relationship between the vigilance decrement and a broad range of physiology measures has not been fully documented. In an attempt to address this gap, an experiment was designed in which participants detected critical signals displayed at random during a 40-minute simulated air traffic control vigilance task. Three localized temperature condition changes, a positive, negative, or no change, were randomly assigned to participants and administered at the halfway point of the task. In addition to collecting performance data, cerebral oximetry, electrocardiography (ECG), and electrooculography (EOG) were utilized to collect a range of physiological signals from participants including cerebral oxygenation levels, heart rate, heart rate variability, blink rate, and interblink intervals. The physiology data when correlated with the decrement indicated by the performance data demonstrated a potential relationship between these measures. By identifying a vigilance decrement in individuals, one or more physiology measures may aid the design of interactive vigilance displays and compensatory measures for overcoming the vigilance decrement

Patient Monitoring Systems

book chapterBiomedical Informatic

Robust Algorithms for Unattended Monitoring of Cardiovascular Health

Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology