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The study evaluates the k-nearest-neighbor (KNN) strategy for the assessment of complexity of the cardiac neural control from spontaneous fluctuations of heart period (HP). Two different procedures were assessed: i) the KNN estimation of the conditional entropy (CE) proposed by Porta et al; ii) the KNN estimation of mutual information proposed by Kozachenko-Leonenko, refined by Kraskov-Stögbauer-Grassberger and here adapted for the CE estimation. The two procedures were compared over HP variability recordings obtained at rest in supine position and during head-up tilt (HUT) in amyotrophic lateral sclerosis patients and healthy subjects. We found that the indexes derived from the two procedures were significantly correlated and both methods were able to detect the effect of HUT on HP complexity within the same group and distinguish the two populations within the same experimental condition. We recommend the use of the KNN strategy to quantify the dynamical complexity of cardiac neural control in addition to more traditional approaches.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor and extra-motor neuron loss. Aim of this study was to attempt a better characterization of the cardiac neural regulation in ALS using the heart rate variability analysis. Cardiac neural regulation was altered in all patients, i.e. they showed a low RR variance and an abnormal response to orthostasis. Interestingly, at rest patients could be divided in those with lower and those with higher sympathetic activity than normal. In both cases the response to orthostasis was blunted. These findings suggest that an impairment of the autonomic nervous system is present in ALS with different patterns of cardiac regulation imbalance among patients. It has to be established if such differences depend upon time course of the disease, different clinical entities, or other anatomic, pathological and clinical variables.