Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results

BACKGROUND: Dizziness and imbalance are common symptoms that are often inadequately diagnosed or managed, due to a lack of dedicated specialists. Decision Support Systems (DSS) may support first-line physicians to diagnose and manage these patients based on personalised data. AIM: To examine the diagnostic accuracy and application of the EMBalance DSS for diagnosis and management of common vestibular disorders in primary care. METHODS: Patients with persistent dizziness were recruited from primary care in Germany, Greece, Belgium and the UK and randomised to primary care clinicians assessing the patients with (+ DSS) versus assessment without (- DSS) the EMBalance DSS. Subsequently, specialists in neuro-otology/audiovestibular medicine performed clinical evaluation of each patient in a blinded way to provide the "gold standard" against which the + DSS, - DSS and the DSS as a standalone tool (i.e. without the final decision made by the clinician) were validated. RESULTS: One hundred ninety-four participants (age range 25-85, mean = 57.7, SD = 16.7 years) were assigned to the + DSS (N = 100) and to the - DSS group (N = 94). The diagnosis suggested by the + DSS primary care physician agreed with the expert diagnosis in 54%, compared to 41.5% of cases in the - DSS group (odds ratio 1.35). Similar positive trends were observed for management and further referral in the + DSS vs. the - DSS group. The standalone DSS had better diagnostic and management accuracy than the + DSS group. CONCLUSION: There were trends for improved vestibular diagnosis and management when using the EMBalance DSS. The tool requires further development to improve its diagnostic accuracy, but holds promise for timely and effective diagnosis and management of dizzy patients in primary care. TRIAL REGISTRATION NUMBER: NCT02704819 (clinicaltrials.gov)

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio