Objective Heightened awareness of Creutzfeldt-Jakob disease (CJD) among physicians and the lay public has led to its frequent consideration in the differential diagnosis of patients with rapidly progressive dementia (RPD). Our goal was to determine which treatable disorders are most commonly mistaken for CJD. Methods We performed a retrospective clinical and neuropathological review of prion-negative brain autopsy cases referred to the US National Prion Disease Pathology Surveillance Center (NPDPSC) at Case Western Reserve University from January 2006 through December 2009. Results Of 1,106 brain autopsies, 352 (32%) were negative for prion disease, 304 of which had adequate tissue for histopathological analysis. Alzheimer disease (154) and vascular dementia (36) were the two most frequent diagnoses. Seventy one patients had potentially treatable diseases. Clinical findings included dementia (42 cases), pyramidal (20), cerebellar (14), or extrapyramidal (12) signs, myoclonus (12), visual disturbance (9) and akinetic mutism (5); a typical electroencephalogram occurred only once. Neuropathological diagnoses included immune-mediated disorders (26), neoplasia (25, most often lymphoma), infections (14), and metabolic disorders (6). Interpretation In patients with RPD, treatable disorders should be considered and excluded before diagnosing CJD. Misdiagnosed patients often did not fulfill WHO criteria. RPD with positive 14-3-3 CSF protein should not be regarded as sufficient for the diagnosis of CJD. Adherence to revised criteria for CJD, which include distinctive MRI features of prion disease, is likely to improve diagnostic accuracy.

The electrical potential produced by the cardiac activity sometimes contaminates electroencephalogram (EEG) recordings, resulting in spiky activities that are referred to as electrocardiographic (EKG) artifact. For a variety of reasons it is often desirable to automatically detect and remove these artifacts. Especially, for accurate source localization of epileptic spikes in an EEG recording from a patient with epilepsy, it is of great importance to remove any concurrent artifact. Due to similarities in morphology between the EKG artifacts and epileptic spikes, any automated artifact removal algorithm must have an extremely low false-positive rate in addition to a high detection rate. In this paper, an automated algorithm for removal of EKG artifact is proposed that satisfies such criteria. The proposed method, which uses combines independent component analysis and continuous wavelet transformation, uses both temporal and spatial characteristics of EKG related potentials to identify and remove the artifacts. The method outperforms algorithms that use general statistical features such as entropy and kurtosis for artifact rejection.

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