Computed Trend Analysis in Automated Patient Monitoring Systems

Hope, Charles E.; Lewis, Colin D.; Perry, I. R.; Gamble, Andrew

doi:10.1093/bja/45.5.440

Cited by 40 publications

(11 citation statements)

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“…A tracking vector comprising of the TTV values of individual features is generated 49 for 10 different changes in the physiological signal at different time-scales.…”

Section: Stress-trend Analysismentioning

confidence: 99%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

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Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra-and inter-subject variability. Task-induced stress-trends were tracked using -based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.

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“…A tracking vector comprising of the TTV values of individual features is generated 49 for 10 different changes in the physiological signal at different time-scales.…”

Section: Stress-trend Analysismentioning

confidence: 99%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

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“…We report on the development, implementation, and prelimina testing of a parallel version of the multi-state Kalman filtering algorithm (KFA) [4]; the KFA appears capable of accurately detecting and identifying ful physiologic trends and discarding certain unwanted physiologic artifcts from the patient record. [6], Patient Condition Factor [7], Exponentially mapped past variables [8], and a Monte Carlo technique [9]. Most of these methods were developed for highly speialized situations and lack general appLicability.…”

Section: Introductionmentioning

confidence: 99%

Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm

Sittig

Factor

1990

Computer Methods and Programs in Biomedicine

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“…This is similar in concept to Hope et al. 's patient condition factor [3] which discriminated between a beneficial change (towards the desired SAP) and an adverse change (away from the desired SAP). In a practical application the desired SAP could be stated at any time to reset the responses.…”

Section: Discussionmentioning

confidence: 64%

“…Hope et al. [3] modified this tracking signal as a result of observations made during measurements of physiological and pharmacological responses to produce a ‘Patient Condition Factor’. They defined upper and lower limits within which the signal should stay and the desired average value for the variable being measured.…”

mentioning

confidence: 99%

Statistics‐based alarms from sequential physiological measurements

Harrison¹,

Connor²

2007

Anaesthesia

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SummaryWe have developed an anaesthesia alarm system that responds in a more clinically appropriate manner than current threshold alarms. A decrease in systolic arterial pressure of 10 mmHg from a previous value of 70 mmHg has a greater clinical significance than a decrease of 10 mmHg from 150 mmHg. However, it has been difficult to envisage a simple algorithm for the detection of these contextually adverse changes in physiological variables. We have processed systolic arterial pressure data to create a mathematically straightforward statistical tool for sampling intervals up to 5 min. Both the blood pressure and the change in blood pressure over a known time interval are plotted on x and y axes with the units in standard deviations. Some 10 824 measurements were obtained at 10-s intervals in 17 patients. The mean (SD) systolic arterial pressure for all observations in our patients was 118 (17.0) mmHg. The mean (SD) change in systolic arterial pressure over 5 min was ) 0.35 (15.2) mmHg. Combining the value for the standard deviation of systolic arterial pressure and the standard deviation of the change in systolic arterial pressure using Pythagoras's theorem creates a value in standard deviations for this particular state. Instead of alarms being set in mmHg, they would be set in standard deviations. This technique was developed further using Principal Component Analysis to isolate uncommon deviations from normal, clinically unimportant physiological variations. These clinically unimportant changes occur in a predictable fashion only if the sampling interval is 90 s or less. This new alarm system is asymmetric -a small decrease in systolic arterial pressure from 90 mmHg may, appropriately, set off an alarm but it would require a much larger increase in systolic arterial pressure to do so.

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Computed Trend Analysis in Automated Patient Monitoring Systems

Cited by 40 publications

References 0 publications

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm

Statistics‐based alarms from sequential physiological measurements

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