Abstract
We hypothesize that changes of shape of arterial blood pressure (ABP) high-frequency waveform signal can be reflective of body's response to stress, in particular to hemorrhage.
Highlights
We hypothesize that changes of shape of arterial blood pressure (ABP) high-frequency waveform signal can be reflective of body’s response to stress, in particular to hemorrhage
When either is present the probability increases to 50%, and if both are present it reaches 92%
To estimate the likelihood of bleeding, our algorithm: (1) Extracts 10 s disjoint intervals of ABP waveform and standardizes the time series to zero mean and unit standard deviation; (2) Discretizes the standardized data into a sequence of symbols each reflective of a particular value of the standardized ABP; (3) Identifies which sequential patterns of symbols are present in the current interval data from among patterns previously extracted from training data using SPADE algorithm [1]; (4) Feed the identified sequential patterns into previously trained Machine Learning (ML) classifier (Random Forest) to predict current bleeding status of the subject
Summary
We hypothesize that changes of shape of arterial blood pressure (ABP) high-frequency waveform signal can be reflective of body’s response to stress, in particular to hemorrhage. Detection of hemorrhage by analyzing shapes of the arterial blood pressure waveforms Introduction We hypothesize that changes of shape of arterial blood pressure (ABP) high-frequency waveform signal can be reflective of body’s response to stress, in particular to hemorrhage.
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