Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generatedfrom different organs can be recorded to extract interesting information about patients’health. The analysis ofphysiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery inDatabases process. The application of such process in the domain of medicine has a series of implications anddifficulties, especially regarding the application of data mining techniques to data, mainly time series, gatheredfrom medical examinations of patients. The goal of this paper is to describe the lessons learned and the experiencegathered by the authors applying data mining techniques to real medical patient data including time series. In thisresearch, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epilepticpatients). We applied a previously proposed knowledge discovery framework for classification purpose obtaininggood results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in ourresearch are the groundwork for the lessons learned and recommendations made in this position paper thatintends to be a guide for experts who have to face similar medical data mining projects.2019-2

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions