Classification of epileptic seizures using accelerometers (2011)

Epilepsy is a neurological disorder that affects a large group of people around the world.The research institute IMEGO has developed a hardware setup for measuring and logging acceleration of limbs on patients suffering from epilepsy.Signal analysis methods are applied to the acceleration data to register when a seizure occurs.In this thesis, the existing seizure classification methods are enhanced by studying the time evolution of the acceleration data over time spans of several minutes around each discrete sample point in time.This is achieved by clustering data and examining the cluster transitions between data points that are close in time.In this work several clustering, classification and dimension reduction techniques are presented.Moreover, a pilot study that assesses the performance of the proposed classification method under a wide variety of parameters, input data and data transformations, is implemented.The old classification method that was used prior to this thesis is also evaluated and compared against the new method developed in this thesis.The pilot study shows that the proposed classification method performs better than the method implemented prior to this work.The proposed method performs well on tonicclonic epileptic seizures even with a sporty and active movement background during the measurements.For strictly tonic seizures, however, the pilot study gives no promising results.It seems like the classification methods are not able to distinguish a tonic seizure from normal activity.The optimal parameters of the proposed method seems to differ depending on the kind of seizure and background data that is studied.In a real application, tuning the parameter model for the appropriate patient behavior seems to be necessary for adequate classification performance.During the measurements, acceleration on three limbs were analyzed.The pilot study reveals that in many cases not all three acceleration points are necessary.Which ones are, depends on the patients seizure behavior.seizures, their causes are still not completely understood.These are known as idiopathic seizures.[1] [2] Seizures by originEpileptic seizures are divided into two major classes depending on the initiation.The partial-onset and generalized-onset seizures. Partial seizuresPartial-onset seizures start in a specific area of the brain.The symptoms depend on the function of that specific area.If the seizure does not alter consciousness it is known as a simple partial seizure.Partial seizures that cloud consciousness and cause abnormal repetitious movements are known as complex partial seizures.Partial seizures are usually associated with some kind of damage to a part of the brain. Generalized seizuresGeneralized seizures are those seizures where the whole or large parts of the brain experience abnormal electrical activity.This kind of seizure is usually much more dramatic given that many of the brain functions are affected at the same time.In many of these types of seizures, the subject will not have any recollection of the seizure afterwards.Generalized seizures occur without evident damage to the brain but they might sometimes be initiated by a partial-onset seizure.If they are caused by a partial-onset seizure they are known as secondary generalized seizures.Clustering method Sensitivity Selectivity Mean STD Mean STD Kmeans, 4 clusters 78.2% 5.0% 72.9% 3.6% Kmeans, 5 clusters 78.2% 5.0% 76.9% 3.8% Kmeans, 3 clusters 80.0% 7.6% 76.1% 5.2% Gm hard, 4 clusters 81.8% 0.0% 67.2% 9.4% Gm hard, 3 clusters 90.9% 9.1% 51.2% 3.3% Gm fuzzy, 3 clusters 85.5% 5.0% 49.1% 6.4% Gm fuzzy, 4 clusters 78.2% 5.0% 56.9% 10.9% Kmeans seizure separated, 3 of 7 clusters 90.9% 6.4% 82.5% 7.6% Kmeans seizure separated, 3 of 7 clusters 83.6% 4.1% 82.4% 5.3% Kmeans seizure separated, 3 of 7 clusters 92.7% 7.6% 83.5% 1.2% Kmeans seizure separated, 3 of 7 clusters 81.8% 0.0% 92.0% 4.5% Kmeans seizure separated, 3 of 7 clusters 98.2% 4.1% 64.2% 6.5% Kmeans seizure separated, 3 of 7 clusters 96.4% 5.0% 63.6% 7.4% Kmeans seizure separated, 3 of 7 clusters 98.2% 4.1% 64.5% 5.5% Kmeans seizure separated, 4 of 7 clusters 85.5% 5.0% 78.5% 3.1% Max values 90.9% 6.4% 75.8% 1.6% Max values 83.6% 4.1% 69.7% 1.0% Min values 74.5% 4.1% 40.4% 3.3% Min values 80.0% 4.1% 52.7% 5.3% Max absolute values 83.6% 4.1% 56.6% 6.4% Max absolute values 87.3% 5.0% 70.9% 5.4%