Data mining and Electroencephalogram

Data Mining(DM):

Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.

Electroencephalogram(EEG):

An electroencephalogram (EEG) is a test used to find problems related to electrical activity of the brain.

An EEG tracks and records brain wave patterns. Small metal discs with thin wires (electrodes) are placed on the scalp, and then send signals to a computer to record the results. Normal electrical activity in the brain makes a recognizable pattern. Through an EEG, doctors can look for abnormal patterns that indicate seizures and other problems.

Why EEG is needed?

EEG is typically used in the following clinical circumstances

• to distinguish epileptic seizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants.
• to differentiate “organic” encephalopathy or delirium from primary psychiatric syndromes such as catatonia
• to serve as an adjunct test of brain death
• to prognosticate, in certain instances, in patients with coma
• to determine whether to wean anti-epileptic medications

Additionally, EEG may be used to monitor certain procedures:

• to monitor the depth of anesthesia
• as an indirect indicator of cerebral perfusion in carotid endarterectomy
• to monitor amobarbital effect during the Wada test

EEG can also be used in intensive care units for brain function monitoring:

• to monitor for non-convulsive seizures/non-convulsive status epilepticus
• to monitor the effect of sedative/anesthesia in patients in medically induced coma (for treatment of refractory seizures or increased intracranial pressure)
• to monitor for secondary brain damage in conditions such as subarachnoid hemorrhage (currently a research method)

Complexity of EEG and why to use DM for EEG:

EEG analysis poses a number of challenges which make it an interesting field of application for DM technology:

· EEG comes in large data bases. E.g. one whole night recording of human sleep result in eight hours of multi-channel data sampled with up to 256Hz

· EEG signals are very noisy.

· EEG signals have a large temporal variance.

· Analysis of EEG data requires the use of range of DM techniques. There are tasks for classification, regression, clustering, sequence, analysis, etc.

DM Approaches:

The Data Mining Methodology was chosen to provide an outline about the study’s life cycle to tackle the stated problem and to describe the processes, techniques and models involved in achieving the study’s goals. The methodology consists of six phases, starting with the initial phase of understanding the project (Introduction), followed by data understanding (EEG Acquisition/Processing), data pre-processing, data modeling, evaluation and ending up with the deployment (knowledge discovery).

However, as with probably any field of application, work published on DM and EEG usually is a mixture of several categories of methods and algorithms, instead of following the more strict categorization above I decided to structure the review into different approaches commonly distinguished within DM are

· Neural networks.

· Machine learning methods.

· Discovery of sequential patterns.

· Statistical approaches.

· Fuzzy and knowledge based approaches.

Conclusion:

This article is written to give you an overview of EEG, DM and approaches of DM for EEG. These initial findings suggest that the proposed automated EEG analytical approach could be a useful adjunctive diagnostic approach in clinical practice.