Detection of crisis in EEG signals with wavelets and weka

We need you to detect epileptic crisis on examples of real data (EEG signals), along with the crisis already detected by a human as an example to calibrate your algorithm (this will be considered the "groundthruth"). You can do that with one of the following programming languages: Matlab or C#. For this task we give you data about where that crisis are, in fact, we give you the solution because what we need is the automatic process to detect more crisis on new EEG files in the future. ## Deliverables **Task Name:** Detection of crisis in EEG signals with wavelets and weka **Summary:** We need you to detect epileptic crisis on examples of real data (EEG signals), along with the crisis already detected by a human as an example to calibrate your algorithm (this will be considered the "groundthruth"). You can do that with one of the following programming languages: Matlab or C#. For this task we give you data about where that crisis are, in fact, we give you the solution because what we need is the automatic process to detect more crisis on new EEG files in the future. **We provide you:** - The signal that we need to analyze ([login to view URL]). - The groundthruth of the signal (model of solution we provide): [login to view URL] - The output example with approximation of solution: [login to view URL] - [login to view URL] is a little application for evaluate your results. We'll use it to test your algorithm. - [login to view URL]: Articles about crisis detection problem. - [login to view URL]: Software to visualize .edf files. - Our Matlab work directory with example algorithm (CrisisDetector.m) which generates output files. We need you to create the same kind of files with the same internal format because is the format that [login to view URL] understands. - Demo application that shows you the process of the signal taken into account only the instant signal values. **We recommend you:** - We recommend you to use wavelets theory to solve this problem, with the rest of the characteristics of the signals like power, amplitude, frequency, etc. - We advance you that the bandwidth between 5Hz-10Hz has interesting information about the signal. - You can use Weka if you considerer it, taken into account the same signal characteristics or any signal characteristic you can consider. **We need from you:** - A solution based in our recommendations. - We need an algorithm which generates output files with the epileptic crisis on the EEG file. - We need that the number of crisis detected will be almost the same as [login to view URL] we provide, with less than 20% of false positives and less than 1% of false negatives. **Deliverables:** We will set the 1st deadline in **one week**. On this date you should give us the first deliverable that must consist on the software needed. In this moment we will test your solution against a new .edf file looking at the results and inform you about the results obtained. If the accuracy is less than 90% we'll let you recalibrate your algorithm and have a second chance. If accuracy detecting crisis in the final algorithm is lower than 2 seconds, we will pay only 10% of the money, since the algorithm is useless for us (assuming no false negatives, and false positives lower than 20% of total crisis). If the error with your given solution is lower than 1 second you will be paid 100% and between 1 second and 2 seconds 50% or you can just follow the next basic steps to obtain 50%, we consider this process as a part of I+D: 1. Deep analysis of the signal in different time-frequency domains (wavelets). 2. Analysis with different granularity (10 tests). 3. Combination of previous analysis with instant signal values. 4. Different combination of these filters. a. Instant signal analysis first and then wavelets analysis /FFT. b. Wavelets analysis / FFT first and then instant signal analysis. **Concepts to take into account:** False negatives: When a crisis is occurred and you don't detect it. False positives: When you detect a crisis and it never occurs. Artifact: As you can see, not all the visually important changes in the signal are crisis, you have to take into account the grounthruth we provide.