A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortality rate among epileptic patients is 2 – 3 times higher than in the matching group of the general population. Wearable devices offer a promising solution for the detection of seizures in real time so as to alert family and caregivers to provide immediate assistance to the patient. However, in order for the detection system to be reliable, a considerable amount of labeled data is needed to train it. Labeling epilepsy data is a costly and time-consuming process that requires manual inspection and annotation of electroencephalogram (EEG) recordings by medical experts. In this paper, we present a self-learning methodology for epileptic seizure detection without medical supervision. We propose a minimally-supervised algorithm for automatic labeling of seizures in order to generate personalized training data. We demonstrate that the median deviation of the labels from the ground truth is only 10.1 seconds or, equivalently, less than 1% of the signal length. Moreover, we show that training a real-time detection algorithm with data labeled by our algorithm produces a degradation of less than 2.5% in comparison to training it with data labeled by medical experts. We evaluated our methodology on a wearable platform and achieved a lifetime of 2.59 days on a single battery charge

AUTOMATIC EEG CLASSIFICATION USING DENSITY BASED ALGORITHMS DBSCAN AND DENCLUE

Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class

Effect of Data Preprocessing in the Detection of Epilepsy using Machine Learning Techniques

1066-1077Epilepsy is the one of the most neurological disorder in our day to day life. It affects more than seventy million people throughout the world and becomes second neurological diseases after migraine. Manual inspection of seizures is time consuming and laborious task. Nowadays automated techniques are evolved for detection of seizures by means of signal processing or through machine learning techniques. In this article, supervised learning algorithms are applied to the EEG dataset and performance are measured in terms of Accuracy, precision and few more. Machine learning algorithm plays a vital role in classification and regression problem in the past few decades. The most important reason for this is a large set of signal or data are trained and the test signals are evaluated using training network. To get the better accuracy, the input data are first normalized carefully. The various normalization techniques applied in this article are Z-Score, Min-Max, Logarithmic and Square Root Normalization. For simulation purpose, Electroencephalography (EEG) signal from UCI Machine Learning Respiratory are used. Dataset consists of 11500 patient details with 5 different cases and each signal are recorded for the duration of 23 seconds. Spider chart is used to show the metric value in detail. It is observed from the result that supervised learning algorithm yields a better result compared to logistic and KNN (K-Nearest Neighbor) algorithm at high iteration

Detection of Pathological HFO Using Supervised Machine Learning and iEEG Data

Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures