The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy

“…Different clinical methods are used to detect seizures such as Magnetic Resonance Imaging [11], Positron Emission Tomography [12], three-dimensional accelerometer [13], and Electroencephalography (EEG). The EEG is the most common method for seizure detection, especially for long-term monitoring since it is cost-effective, fast, and does not have any side effects.…”

Section: Importance and Challenges Of Epilepsy Detectionmentioning

confidence: 99%

“…Information is equal to negative of entropy and is defined as: 13) where ( ) is the prior probability for all values and in the discrete contribution it is equal to the fraction of samples in the class to all samples. Also information on the joint distribution of classes and features is defined as: 14) where ( ) is the joint probabilities of feature vector after the values (target) are given.…”

Section: Relevance Indices Based On Information Theorymentioning

confidence: 99%

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Zabihi

Kıranyaz

İnce

et al. 2013

IET Intelligent Signal Processing Conference 2013 (ISP 2013)

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Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem.Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers.The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient's EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM.Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively. PREFACEThis master thesis was carried out in the MUVIS group at the signal processing department of Tampere University of Technology. It was funded by the Academy of Finland.I wish to express my sincere gratitude to Professor Serkan Kiranyaz, whose inspiring guidance and inexhaustible patience have helped me cultivate a systematic approach towards the research. I am indebted to him for the freedom he gave me in implementing my ideas. I have also acquired valuable insights through his instruction in academic approach.I am very much grateful to Professor Turker Ince for his warm support and his valuable suggestions. Besides, I would also like to thank Professor Moncef Gabbouj for providing me with the opportunity for the thesis work. He gave me a lot of trust and flexibility in the project.I have been extremely fortunate in having valuable friends who have made a homely atmosphere for me during my stay in Tampere

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“…Nijsen et al [7] used accelerometers (ACM) for seizure detection. The visual analysis of the movement data recorded with these sensors showed promising results (91% of the seizures with motor phenomena were detected), and was considered feasible for detection of seizures.…”

Section: Introductionmentioning

confidence: 99%

“…Earlier studies [7][8][9] on detecting seizures from ACM data did not report on aspects concerning the time between seizure onset and the detection, but only on detection versus no detection. It is highly desirable to achieve an early detection of seizures (i.e.…”

Section: Introductionmentioning

confidence: 99%

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

Conradsen¹,

Beniczky²,

Wolf³

et al. 2012

Computer Methods and Programs in Biomedicine

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The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy

Cited by 129 publications

References 15 publications

An Image Processing Based Novel Approach to Real-Time Analysis and Diagnosis of Epileptic Seizures using Wearable Sensors

An Image Processing Based Novel Approach to Real-Time Analysis and Diagnosis of Epileptic Seizures using Wearable Sensors

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

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