Leave-one-out prediction error of systolic arterial pressure time series under paced breathing.
In this paper, we consider systolic arterial pressure time series from healthy subjects and chronic heart failure patients, undergoing paced respiration, and show that different physiological states and pathological conditions may be characterized in terms of predictability of time series signals from the underlying biological system. We model time series by the regularized least-squares approach and quantify predictability by the leave-one-out error. We find that the entrainment mechanism connected to paced breath, that renders the arterial blood pressure signal more regular and thus more predictable, is less effective in patients, and this effect correlates with the seriousness of the heart failure. Using a Gaussian kernel, so that all orders of nonlinearity are taken into account, the leave-one-out error separates controls from patients (probability less than 10(-7)), and alive patients from patients for whom cardiac death occurred (probability less than 0.01).
Ancona
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Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Bari, Italy.
Maestri
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R
Marinazzo
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D
Nitti
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L
Pellicoro
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M
Pinna
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Stramaglia
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D000465:Algorithms; D001794:Blood Pressure; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D012119:Respiration; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D013223:Statistics as Topic; D013599:Systole