Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Pelvic radiography in ATLS algorithms: A diminishing role?

ABSTRACT: BACKGROUND: Pelvic x-ray is a routine part of the primary survey of polytraumatized patients according to Advanced Trauma Life Support (ATLS) guidelines. However, pelvic CT is the gold standard imaging technique in the diagnosis of pelvic fractures. This study was conducted to confirm the safety of a modified ATLS algorithm omitting pelvic x-ray in hemodynamically stable polytraumatized patients with clinically stable pelvis in favour of later pelvic examination by CT scan. METHODS: We conducted a retrospective analysis of all polytraumatized patients in our emergency room between 01.07.2004 and 31.01.2006. Inclusion criteria were blunt abdominal trauma, initial hemodynamic stability and a stable pelvis on clinical examination. We excluded patients requiring immediate intervention because of hemodynamic instability. RESULTS: We reviewed the records of n = 452 polytraumatized patients, of which n = 91 fulfilled inclusion criteria (56% male, mean age = 45 years). The mechanism of trauma included 43% road traffic accidents, 47% falls. In 68/91 (75%) patients, both a pelvic x-ray and a CT examination were performed; the remainder had only pelvic CT. In 6/68 (9%) patients, pelvic fracture was diagnosed by pelvic x-ray. None of these 6 patients was found having a false positive pelvic x-ray, i.e. there was no fracture on pelvic CT scan. In 3/68 (4%) cases a fracture was missed in the pelvic x-ray, but confirmed on CT (false negative on x-ray). None of the diagnosed fractures needed an immediate therapeutic intervention. 5 (56%) were classified type A fractures, and another 4 (44%) B 2.1 in computed tomography (AO classification). One A 2.1 fracture was found in a clinically stable patient who only received CT scan (1/23). CONCLUSION: While pelvic x-ray is an integral part of ATLS assessment, this retrospective study suggests that in hemodynamically stable patients with clinically stable pevis, its sensitivity is only 67% and it may safely be omitted in favor of a pelvic CT examination if such is planned in adjunct assessment and available. The results support the safety and utility of our modified ATLS algorithm. A randomized controlled trial using the algorithm can safely be conducted to confirm the results.

GPI-specific phospholipase D (GPI-PLD) is expressed during mouse development and is localized to the extracellular matrix of the developing mouse skeleton

Glycosylphosphatidylinositol-specific phospholipase D (GPI-PLD) is abundant in serum and has a well-characterized biochemistry; however, its physiological role is completely unknown. Previous investigations into GPI-PLD have focused on the adult animal or on in vitro systems and a putative role in development has been neither proposed nor investigated. We describe the first evidence of GPI-PLD expression during mouse embryonic ossification. GPI-PLD expression was detected predominantly at sites of skeletal development, increasing during the course of gestation. GPI-PLD was observed during both intramembraneous and endochondral ossification and localized predominantly to the extracellular matrix of chondrocytes and to primary trabeculae of the skeleton. In addition, the mouse chondrocyte cell line ATDC5 expressed GPI-PLD after experimental induction of differentiation. These results implicate GPI-PLD in the process of bone formation during mouse embryogenesis.

Characterization of the macromolecule baseline in localized (1)H-MR spectra of human brain

Short-echo-time magnetic resonance spectra of human brain contain broad contributions from macromolecules. As they are a priori of unknown shape and intensity, they pose a problem if one wants to quantitate the overlying spectral features from low-molecular-weight metabolites. On the other hand, the macromolecular contributions may provide relevant clinical information themselves, if properly evaluated. Several methods, based on T(1), T(2), or spectral shape, have previously been suggested to suppress or edit the macromolecule contributions. Here, a method is presented based on a series of saturation recovery scans and that allows for simultaneous recording of the macromolecular baseline and the fully relaxed metabolite spectrum. In comparison to an inversion recovery technique aimed at nulling signals from long-T(1) components, the saturation recovery method is less susceptible to T(1) differences inherent in signals from different metabolites or introduced by pathology. The saturation recovery method was used to quantitate the macromolecular baseline in white and/or gray matter locations of the human brain in 40 subjects. It was found that the content and composition of MR visible macromolecules depends on cerebral location, as well as the age of the investigated subject, while no gender dependence could be found.