Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

Clinical factors that affect walking level and performance in chronic spinal cord lesion patients.

STUDY DESIGN: Observational Study. OBJECTIVE: To evaluate the effects of neurologic and non-neurologic factors on walking level and performance in chronic spinal cord lesion (SCL) patients. SUMMARY OF BACKGROUND DATA: Walking is one of the primary goals of patients after a SCL. Several studies have demonstrated that different neurologic and non-neurologic factors can affect walking level and performance. However, in SCL age and muscle strength have always been considered the major determinants of walking. METHODS: Sixty-five patients with chronic SCL were included. Their demographic, neurologic status (ASIA standards), balance, and spasticity were recorded. Pearson and Spearman correlations were adopted to quantify the association between patients' characteristics and walking ability. The relationship between functional walking measures, Timed Up and Go, Six Minutes Walking Test (SMWT), Ten Meters Walking Test, and Walking Index for Spinal Cord Injury, and demographic and neurologic factors were measured by regression analyses. RESULTS: Strength, balance, spasticity, and age were strictly correlated with walking level and walking performance. They also were the best predictors of walking features. CONCLUSION: Results confirm the recognized importance of age and upper and lower extremity strengths for walking after a SCL. They also highlight the role of 2 other factors, i.e., balance and spasticity, seldom considered as thoroughly in SCL.