Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Prediction of functional outcome after spinal cord injury: a task for the rehabilitation team and the patient

Study design: Descriptive analysis of data gathered in an information system.Objectives: To explore the predictions of professionals and patients regarding functional outcome after spinal cord injury related to the final results after inpatient rehabilitation, in order to make prognostics of rehabilitation outcome more successful and enlarge the role of the patient in selecting realistic rehabilitation goals.Methods: Data from 55 patients with spinal cord injury admitted to the rehabilitation centre. Expectations of the rehabilitation team and the patients regarding future independence in performing six daily activities were compared to the functional results at discharge. The results of patients with different level and extent of lesion were analyzed.Results: In 52% of all performed skills, independence was achieved at discharge. Professionals and patients made similar predictions. If they both expected independence after rehabilitation, 90% of the skills were performed independently at discharge. If they both did not expect independence only 3% of the functional results were positive. Of all combined predictions 64% was correct. Correct predictions were most often found regarding self-care skills of patients with paraplegia and regarding mobility of patients with complete lesions. Prediction of self-care outcome of patients with tetraplegia is far more complicated. There was a considerable variation in predictions of mobility potential, especially regarding patients with incomplete lesions. If the team and patients agreed upon expected independence in mobility skills of these patients, the final results were mostly positive.Conclusions: Prediction of functional outcome after spinal cord injury was most successful if the expectations of the team and patients were combined. Prognosis of self-care outcome of patients with paraplegia and mobility potential of patients with complete spinal cord lesions was usually clear at admission. However, selection of realistic goals concerning self-care skills of patients with tetraplegia and mobility skills of patients with incomplete lesions is far more complicated. Gradual adjustment of objectives is needed during the rehabilitation process in close collaboration between the professionals and the patients

Seizure-onset mapping based on time-variant multivariate functional connectivity analysis of high-dimensional intracranial EEG : a Kalman filter approach

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (< 60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach