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Objective and personalized longitudinal assessment of a pregnant patient with post severe brain trauma.

Torres EB, Lande B - Front Hum Neurosci (2015)

Bottom Line: These patterns could blindly identify the time preceding the baby's delivery by C-section when the patient systematically brought her hand to her abdominal area.Changes in temperature were sharp and accompanied by systematic changes in the statistics of the motions that rendered her dominant wrist's micro-movements more systematically reliable and predictable than those of the non-dominant writst.The new analytics paired with wearable sensing technology may help track the day-by-day individual progression of a patient with post brain trauma in clinical settings and in the home environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Computational Biomedicine Imaging and Modeling Center, Computer Science, Neuroscience and Rutgers Center for Cognitive Science, Rutgers the State University of New Jersey New Brunswick, NJ, USA.

ABSTRACT

Background: Following severe trauma to the brain (whether internally generated by seizures, tumors or externally caused by collision with or penetration of objects) individuals may experience initial coma state followed by slow recovery and rehabilitation treatment. At present there is no objective biometric to track the daily progression of the person for extended periods of time.

Objective: We introduce new analytical techniques to process data from physically wearable sensors and help track the longitudinal progression of motions and physiological states upon the brain trauma. Setting and Participant: The data used to illustrate the methods were collected at the hospital settings from a pregnant patient in coma state. The patient had brain trauma from a large debilitating seizure due to a large tumor in the right pre-frontal lobe.

Main measures: We registered the wrist motions and the surface-skin-temperature across several daily sessions in four consecutive months. A new statistical technique is introduced for personalized analyses of the rates of change of the stochastic signatures of these patterns.

Results: We detected asymmetries in the wrists' data that identified in the dominant limb critical points of change in physiological and motor control states. These patterns could blindly identify the time preceding the baby's delivery by C-section when the patient systematically brought her hand to her abdominal area. Changes in temperature were sharp and accompanied by systematic changes in the statistics of the motions that rendered her dominant wrist's micro-movements more systematically reliable and predictable than those of the non-dominant writst.

Conclusions: The new analytics paired with wearable sensing technology may help track the day-by-day individual progression of a patient with post brain trauma in clinical settings and in the home environment.

No MeSH data available.


Related in: MedlinePlus

Noise analyses to separate predictable and reliable from random and noisy motion data: The minute by minute variability is obtained for the maximal deviations from the mean acceleration, taken for each °C interval. (A) Top panel is the matrix of maximal deviations from the mean linear acceleration (explained in Figure 2) within the temperature regime of motions. Bottom panel is the matrix of the noise-to-signal ratio (the Fano Factor: the estimated Gamma variance divided by the estimated Gamma mean) obtained from the estimated shape and scale parameters of the continuous Gamma family of probability distributions. The highest motion regime occurs between 33°C and 35°C. The highest noise regime occurs at 25°C while the lowest noise regime occurs at 32°C. (B) The frequency histograms of the noise-to-signal values are color coded in order of increasing temperature values. Colors are in correspondence to the points on the Gamma plane in (C). The red star marks the highest noise-to-signal regime while the blue star marks the lowest regime. The temperature intervals containing the highest motion patterns are enclosed by a rectangle. These correspond to the three right most points in the Gamma plane (most systematic patterns), also enclosed within a rectangle.
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Figure 4: Noise analyses to separate predictable and reliable from random and noisy motion data: The minute by minute variability is obtained for the maximal deviations from the mean acceleration, taken for each °C interval. (A) Top panel is the matrix of maximal deviations from the mean linear acceleration (explained in Figure 2) within the temperature regime of motions. Bottom panel is the matrix of the noise-to-signal ratio (the Fano Factor: the estimated Gamma variance divided by the estimated Gamma mean) obtained from the estimated shape and scale parameters of the continuous Gamma family of probability distributions. The highest motion regime occurs between 33°C and 35°C. The highest noise regime occurs at 25°C while the lowest noise regime occurs at 32°C. (B) The frequency histograms of the noise-to-signal values are color coded in order of increasing temperature values. Colors are in correspondence to the points on the Gamma plane in (C). The red star marks the highest noise-to-signal regime while the blue star marks the lowest regime. The temperature intervals containing the highest motion patterns are enclosed by a rectangle. These correspond to the three right most points in the Gamma plane (most systematic patterns), also enclosed within a rectangle.

Mentions: In Figure 4 we continue to use the May 8th matrix to further illustrate the methods. We use the range from 33–35°C to show the statistics of the motion. For each °C we count the number of maximal deviations (peaks) across the session (6.26-h or 375 min along the rows of the matrix) and gather them in a frequency histogram. For each of the histograms representing the motions for each °C-interval we then fit a probability distribution function. Using maximum likelihood estimation (MLE) we obtain estimates of the shape (a) and the scale (b) parameters of the Gamma probability distribution with 95% confidence intervals. (We have used the continuous Gamma family of probability distributions in previous work to characterize the range of human motion variability across a range of neurological disorders and typical motions). From the Gamma estimated parameters we obtain the Gamma statistical parameters (mean and variance) and plot them on a (μ, σ)-plane. Each point represents the Gamma statistical parameters of the acceleration-dependent motions for a temperature °C-interval taken across the time length of the session.


Objective and personalized longitudinal assessment of a pregnant patient with post severe brain trauma.

Torres EB, Lande B - Front Hum Neurosci (2015)

Noise analyses to separate predictable and reliable from random and noisy motion data: The minute by minute variability is obtained for the maximal deviations from the mean acceleration, taken for each °C interval. (A) Top panel is the matrix of maximal deviations from the mean linear acceleration (explained in Figure 2) within the temperature regime of motions. Bottom panel is the matrix of the noise-to-signal ratio (the Fano Factor: the estimated Gamma variance divided by the estimated Gamma mean) obtained from the estimated shape and scale parameters of the continuous Gamma family of probability distributions. The highest motion regime occurs between 33°C and 35°C. The highest noise regime occurs at 25°C while the lowest noise regime occurs at 32°C. (B) The frequency histograms of the noise-to-signal values are color coded in order of increasing temperature values. Colors are in correspondence to the points on the Gamma plane in (C). The red star marks the highest noise-to-signal regime while the blue star marks the lowest regime. The temperature intervals containing the highest motion patterns are enclosed by a rectangle. These correspond to the three right most points in the Gamma plane (most systematic patterns), also enclosed within a rectangle.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4362300&req=5

Figure 4: Noise analyses to separate predictable and reliable from random and noisy motion data: The minute by minute variability is obtained for the maximal deviations from the mean acceleration, taken for each °C interval. (A) Top panel is the matrix of maximal deviations from the mean linear acceleration (explained in Figure 2) within the temperature regime of motions. Bottom panel is the matrix of the noise-to-signal ratio (the Fano Factor: the estimated Gamma variance divided by the estimated Gamma mean) obtained from the estimated shape and scale parameters of the continuous Gamma family of probability distributions. The highest motion regime occurs between 33°C and 35°C. The highest noise regime occurs at 25°C while the lowest noise regime occurs at 32°C. (B) The frequency histograms of the noise-to-signal values are color coded in order of increasing temperature values. Colors are in correspondence to the points on the Gamma plane in (C). The red star marks the highest noise-to-signal regime while the blue star marks the lowest regime. The temperature intervals containing the highest motion patterns are enclosed by a rectangle. These correspond to the three right most points in the Gamma plane (most systematic patterns), also enclosed within a rectangle.
Mentions: In Figure 4 we continue to use the May 8th matrix to further illustrate the methods. We use the range from 33–35°C to show the statistics of the motion. For each °C we count the number of maximal deviations (peaks) across the session (6.26-h or 375 min along the rows of the matrix) and gather them in a frequency histogram. For each of the histograms representing the motions for each °C-interval we then fit a probability distribution function. Using maximum likelihood estimation (MLE) we obtain estimates of the shape (a) and the scale (b) parameters of the Gamma probability distribution with 95% confidence intervals. (We have used the continuous Gamma family of probability distributions in previous work to characterize the range of human motion variability across a range of neurological disorders and typical motions). From the Gamma estimated parameters we obtain the Gamma statistical parameters (mean and variance) and plot them on a (μ, σ)-plane. Each point represents the Gamma statistical parameters of the acceleration-dependent motions for a temperature °C-interval taken across the time length of the session.

Bottom Line: These patterns could blindly identify the time preceding the baby's delivery by C-section when the patient systematically brought her hand to her abdominal area.Changes in temperature were sharp and accompanied by systematic changes in the statistics of the motions that rendered her dominant wrist's micro-movements more systematically reliable and predictable than those of the non-dominant writst.The new analytics paired with wearable sensing technology may help track the day-by-day individual progression of a patient with post brain trauma in clinical settings and in the home environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Computational Biomedicine Imaging and Modeling Center, Computer Science, Neuroscience and Rutgers Center for Cognitive Science, Rutgers the State University of New Jersey New Brunswick, NJ, USA.

ABSTRACT

Background: Following severe trauma to the brain (whether internally generated by seizures, tumors or externally caused by collision with or penetration of objects) individuals may experience initial coma state followed by slow recovery and rehabilitation treatment. At present there is no objective biometric to track the daily progression of the person for extended periods of time.

Objective: We introduce new analytical techniques to process data from physically wearable sensors and help track the longitudinal progression of motions and physiological states upon the brain trauma. Setting and Participant: The data used to illustrate the methods were collected at the hospital settings from a pregnant patient in coma state. The patient had brain trauma from a large debilitating seizure due to a large tumor in the right pre-frontal lobe.

Main measures: We registered the wrist motions and the surface-skin-temperature across several daily sessions in four consecutive months. A new statistical technique is introduced for personalized analyses of the rates of change of the stochastic signatures of these patterns.

Results: We detected asymmetries in the wrists' data that identified in the dominant limb critical points of change in physiological and motor control states. These patterns could blindly identify the time preceding the baby's delivery by C-section when the patient systematically brought her hand to her abdominal area. Changes in temperature were sharp and accompanied by systematic changes in the statistics of the motions that rendered her dominant wrist's micro-movements more systematically reliable and predictable than those of the non-dominant writst.

Conclusions: The new analytics paired with wearable sensing technology may help track the day-by-day individual progression of a patient with post brain trauma in clinical settings and in the home environment.

No MeSH data available.


Related in: MedlinePlus