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Recovering Information from Physiologic Time-series Data. Philip Crooke Department of Mathematics. Data-Models-Insight. Outline. NIV: an example of a simple model that has complicated output. Stress Index: using an explicit mathematical model to confirm a data mining observation.
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Recovering Information from Physiologic Time-series Data Philip Crooke Department of Mathematics
Outline • NIV: an example of a simple model that has complicated output. • Stress Index: using an explicit mathematical model to confirm a data mining observation. • Sleep Apnea: decoding time-series data with pattern recognition. • A new project that combines data mining and mathematical models.
Importance of Noninvasive Ventilation (NIV) to Patient Care Objective: To present a meta-analytic update on the effects of noninvasive ventilation in the management of acute respiratory failure. Design: Meta-analysis of randomized controlled clinical trials in acute respiratory failure comparing NIV with standard medical therapy. Patients: Randomized controlled trials of NIV in acute respiratory failure were identified by search of (i) MEDLINE (1966-2000), (ii) published abstracts from scientific meetings, and (iii) bibliographies of relevant articles. Measurements and Main Results: …..15 randomized controlled trials … Conclusion:Substantial reductions in mortality and the need of subsequent MV were associated with NIV in acute respiratory failure, especially in the COPD subgroup. Hospital length of stay was variably affected. Heterogeneity of treatment effects was observed. From J.V. Peter et al., Noninvasive ventilation in acute respiratory failure—A meta-analysis update, Crit. Care Med. 30, pp. 555-562, 2002.
Patient-Ventilator Asynchrony • Noninvasive Ventilation: Ventilation without endotracheal intubation. • Synchrony: Parallelism between the cycle timing and flow demands of the patient and the responses of the mechanical ventilator
Ventilator-Patient Interaction • Constant Pressure Ventilation • Ventilator applies constant pressure until the flow into the patient is some fraction (a) of the initial flow • Ventilator turns off and expiration starts • Characteristics: variable inspiratory time and variable tidal volume and end-expiratory pressure
Inspiratory Times for Different Cutoff Values Point: Simple linear model has complicated behavior.
The Stress Index (Ranieri) Objective: To evaluate whether the shape of the airway pressure-time curve during constant flow inflation corresponds to evidence of tidal recruitment or tidal hyperinflation in an experimental model of acute lung injury. Model: Conclusion: Tidal Recruitment when and hyperinflation when . Reference: P.S. Crooke, J.J. Marini and J.R. Hotchkiss, A new look at the stress index for lung injury, J. Biol. Sci.13(2005), 261-272.
Concavity of Airway Pressure Tidal Recruitment (b<1): Hyperinflation (b>1):
Classification of Inspiratory Flows by Finite Automata • Diagnosing sleep apnea with nasal prongs • Breath-by-breath analysis for soft tissue collapse in upper airway during sleep • Use syntactic pattern recognition methods • Reference: T. Aittokallio et al., Classification of nasal inspiratory flow shapes by attributed finite automata, Comp. Biomed. Res.32(1999), 34-55.
Waveforms Types 2 - two humps 3 - three humps 12 - one hump/flat spot 13 - flat spot/hump 14 - flat spot/hump/flat spot 111 - one hump (no flat spot) 112 - one hump (big flat spot)
Transition Function: δ[q[0], a] := {q[1], Null}; δ[q[0], b] := {q[0], Null}; δ[q[1], a] := {q[1], Null}; δ[q[1], b] := {q[2], Null}; δ[q[1], c] := {q[3], h}; δ[q[2], a] := {q[1], t}; δ[q[2], b] := {q[2], Null}; δ[q[2], c] := {q[3], h}; δ[q[3], a] := {q[1], Null}; δ[q[3], b] := {q[4], t}; δ[q[3], c] := {q[3], Null}; δ[q[4], a] := {q[1], Null}; δ[q[4], b] := {q[4], Null}; δ[q[4], c] := {q[3], Null};
Train and Test • Compare patterns of controls and patients with partial upper airway obstruction • Find and (another parameter used in separating classes 111 and 112) to identify the highest percentage of obstructive breaths (3623 total).
Automated Search Program for Breathing Pattern Analysis Rationale: More nuanced interpretation of breathing patterns could have diagnostic, prognostic, and interventional benefits. Hypothesis: The breathing patterns adopted by individuals having specific physiologic characteristics (such as cardiac output and neurological conditions) is constrained by their regulatory systems and their impedance characteristics. Problem: The system, although low-dimensional by many standards, is sufficiently high dimensional that patterns are very difficult to identify or classify by human inspection of physiologic tracings. The tracings are long (hours) and contain many breaths. Moreover, there is considerable noise and interpatient variability. Approach: Apply automated (“machine learning”) algorithms to search existing and current databases to identify breathing patterns associated with specific diagnostic or prognostic categories (sleep apnea, heart failure, neurological failure, and ventilator intolerance). Methods: An automated search algorithm has been constructed that compares symbol sequences derived from physiologic tracings and identifies recurrent symbol motifs within these sequences. The sequences can be from the same patient (seeking recurrent patterns within that patient), different patients (to identify patterns that are common to a particular diagnostic or prognostic category), or a mixture of both.
Samples EKG EEG Dynamic Volume Pressure Leg Movement Snoring Blood Oxygen Etc.
