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estimating the signal variability of heart rate observations
2
. Monitor-
ing reductions in Heart Rate Variability (HRV) has been a successful
strategy for the early detection of disorders of the central and peripheral
nervous system that induce a pro-inflammatory response [43].
Figure 14.3.
Sensing in intensive care environments
Existing efforts to model the inflammatory response are focused pri-
marily on the one dimensional HRV analysis, due to a large body of work
on ECG waveform processing. The Society for Complexity in Acute
Illness (SCAI) [46] has devoted many efforts to model complexity and
variability in the human body from ECG signals, as a way to model ICU
patients and derive models predicting complications in ICUs. Variabil-
ity metrics [47] typically used include spectral analysis techniques [52],
approximations to uncomputable notions of randomness with the ap-
proximate and sample entropy [48],[44], and fractal analysis techniques
like the Detrended Fluctuation Analysis [49]. Surprisingly, classical in-
formation theoretic approaches to measure complexity with well under-
stood concepts of compressibility and predictability [50] have received a
modest amount of attention in acute care.
2
Reductions in the variability of other vital signs such as respiration may also be corre-
lated with the inflammatory response.
