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|Title:||Smart sensor for EEG acquisition and epileptic seizure detection with on-chip analog classifier||Authors:||Dinup Sukumaran||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Integrated circuits||Issue Date:||2013||Abstract:||Epilepsy is a chronic neurological disorder affecting approximately 1% of the world’s population, which predisposes those affecting to experiencing recurrent seizures. Despite advances in currently available treatments like anti-epileptic drugs and epilepsy surgery, 25% of the patients continue to have disabling seizures. These unpredictable and medically intractable seizures make an epileptic patient susceptible to high risk of sustaining physical injuries. A reliable real-time seizure detection/prediction system that can drive an antiepileptic device or alert the patient/caregiver about the coming seizure would provide a better quality of life for epileptic patients. Our research work discusses the design of a wearable smart sensor for real-time, patient-specific epileptic seizure detection through the continuous monitoring of non-invasive EEG. In this thesis, we are proposing a smart sensor architecture for epileptic seizure detection. We first present a novel patient specific epileptic seizure detection algorithm, which employs individual Extreme Learning Machine (ELM) classifiers at each EEG electrode/channel to classify the feature vectors extracted from each EEG channel to seizure or non-seizure. Classification outputs from individual EEG channels are categorized at a master classifier to detect the seizure onset. Spectral energy in different frequency bands of EEG signal is used as a feature vector. This algorithm has the advantage of using low dimensional feature space for classification reducing the complexity of higher dimensional classification with a single classifier. Also it provides flexibility to choose the electrodes required to make the seizure prediction system for a particular patient during training which can reduce the amount of data used for classification, increasing training speed and performance of the classifier. The proposed algorithm is validated using 109 hours of seizure EEG data of three patients with 19 seizures, and we achieved sensitivity, specificity and latency of 100%, 16 (false alarms per hour) and 3.6 seconds respectively. We also do a system simulation for different methodologies of feature vector extraction to obtain specifications for hardware implementation of the algorithm. We also propose a hardware architecture for the algorithm implementation in analog integrated circuits. A smart sensor IC designed in AMS 0.35um CMOS for real time EEG acquisition from a scalp EEG channel electrode, feature vector extraction and ELM classification for on-chip seizure onset detection using low power analog integrated circuit topologies is presented. The novelty of this design compared to existing architectures is the use of an on-chip spiking neuron based ELM classifier. ELM possesses similar classification capabilities as a support vector machine (SVM), but requires less nodes for classification. Also ELM requires random weights in the first stage which can be obtained from the inherent threshold voltage variations of transistors. The mismatch inherent in analog VLSI circuits, which is seen as a disadvantage in most of the analog designs, is thus exploited in ELM hardware implementation. Simulation results show the integration of signal processing and classification at the sensor node itself can reduce the power consumption by 75% compared to existing seizure detector architectures. Local processing for different EEG pattern recognition/classification at the sensor node itself can also be used in prompt feedback applications like Brain to Computer Interface (BCI) applications. On-chip data reduction and low power operation makes this approach suitable for wearable EEG applications like long term EEG recordings for clinical diagnosis.||URI:||http://hdl.handle.net/10356/54811||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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