Mentis Cura's proprietary technology is positioned to dramatically improve access to affordable brain diagnostics.
Specialising in EEG statistical pattern recognition we have developed unique diagnostic aids by combining cutting-edge machine learning and our database of EEG recordings from over 3,000 persons followed clinically for over a decade.
Mentis Cura products draw on Mentis Cura’s vast databases of more than 3,000 EEG recordings and their detailed analysis. All clinical cases are based on a consensus clinical diagnosis, according to standard clinical guidelines.
The Dementia database consists of individuals from the community without a diagnosis of dementia and clinically diagnosed individuals that visited the Memory Clinic at the Landspitali University Hospital in Reykjavik, Iceland over a 10-year period. The clinical diagnosis include individuals with mild cognitive impairment (MCI) Alzheimer’s disease (in the prodromal, mild, moderate, and severe stages), vascular dementia, dementia with Lewy bodies, Parkinson’s disease dementia, mixed cases of Alzheimer's disease and vascular dementia, frontotemporal dementia, depression, multiple sclerosis and other. The databases is dynamic in nature and is updated regularly with respect to changes in clinical diagnosis and addition of new cases. To date, the database supporting differential dementia diagnosis consist of EEG recordings from individuals between 50-90 years of age.
The ADHD-I database consist of EEG recordings from children without an ADHD diagnosis and children diagnosed with a developmental and/or psychiatric disorder either at the Child and Adolescent Psychiatric department at Landspitali University Hospital or the Center for Child Development and Behavior in Reykjavik Iceland. The clinical diagnosis include Attentional deficit hyperactive disorder (ADHD), anxiety, depression, autism, Tourette, oppositional defiant disorder, obsessive compulsion disorder and other. To date, the database consists of over 1200 EEG recordings from children aged 6-17 years of age.
The ADHD-II database stems from adult individuals without an ADHD diagnosis and individuals diagnosed with ADHD and/or psychiatric disorders at the Psychiatric Center of Landspítali University Hospital in Iceland. They include recording from people diagnosed with ADHD, anxiety, depression, bipolar disorder, substance abuse, personality disorder and other. To date, the database consists of over 700 EEG recordings from individuals aged 18-60 years of age, subjects are still being recruited.
Mentis Cura relies on EEG data recorded with electrode placement according to the International 10-20 System. The recording is made while the patient is resting with eyes closed. For each EEG measurement 19 electrodes are used individually for analysis, as well as 37 electrode pairs gauging coherence features. For each of the said signals 20 spectral features are extracted. Thus a total of 1,120 features are extracted from each EEG recording.
A classifier is then created for each possible pair of groups using statistical pattern recognition (SPR) in combination with genetic algorithms. A 10-fold cross-validation approach is then used to obtain average values for accuracy, sensitivity, and specificity for each classifier.
Mentis Cura relies on the International 10-20 System of electrode placement. Each EEG recording results in data gathered during a five minute recording session where the patient is at rest with eyes closed.
Fast Fourier Transformation is applied to the EEGs to assess the power spectrum for each channel of the EEG. Overlapping triangular windows are then applied to generate frequency bands from which power spectrum and coherence values were determined resulting in the 1,120 features extracted. These features are then used for further analysis.
A classifier is created for each possible pair of groups using statistical pattern recognition (SPR) in combination with genetic algorithms which search for optimal combinations of features to be used in the classifiers. A 10-fold cross-validation approach is then applied to obtain estimates for accuracy, sensitivity, and specificity for each classifier.
A fixed number of EEG features are used for the construction of each index, select by the genetic algorithm, and the number of features used can vary depending on the patient groups under consideration. Each index is based on a classifier constructed using support vector machines with linear kernel function. The decision function is then scaled to have the desired cut off, resulting in a continuous marker with a specific cut off for decision making.
Sigla and Katla generate results in matter of minutes that provide physicians with an easy-to-read analysis of the EEG recording from their patient compared to Mentis Cura’s vast database of EEGs.
Sigla offers valuable support to those engaged in the field of dementia and the report consists of two sets of indices:
The Dementia index indicates whether and individual's EEG signals resemble those of individuals suffering from dementia.
The Lewy body index is designed to differentiate between dementia with Lewy bodies and other types of dementia. It is a very helpful tool to both identifying Lewy body dementias and for ruling out Lewy body dementias from other types of dementia.
Katla offers valuable support to those engaged in the field of developmental and psychiatric disorders of children and the report consists of one index:
The ADHD Index indicates whether or not a child's EEG resembles that of children diagnosed with ADHD (according to DSM-IV).