Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model

Performance metrics for the accurate characterisation of interictal spike detection algorithms.

Accepted versio

Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification

Automated analysis of brain activity from electroencephalogram (EEG) has indispensable applications in many fields such as epilepsy research. This research has studied the abilities of negative selection and clonal selection in artificial immune system (AIS) and particle swarm optimization (PSO) to produce different reliable and efficient methods for EEG-based epileptic seizure recognition which have not yet been explored. Initially, an optimization-based classification model was proposed to describe an individual use of clonal selection and PSO to build nearest centroid classifier for EEG signals. Next, two hybrid optimization-based negative selection models were developed to investigate the integration of the AIS-based techniques and negative selection with PSO from the perspective of classification and detection. In these models, a set of detectors was created by negative selection as self-tolerant and their quality was improved towards non-self using clonal selection or PSO. The models included a mechanism to maintain the diversity and generality among the detectors. The detectors were produced in the classification model for each class, while the detection model generated the detectors only for the abnormal class. These hybrid models differ from each other in hybridization configuration, solution representation and objective function. The three proposed models were abstracted into innovative methods by applying clonal selection and PSO for optimization, namely clonal selection classification algorithm (CSCA), particle swarm classification algorithm (PSCA), clonal negative selection classification algorithm (CNSCA), swarm negative selection classification algorithm (SNSCA), clonal negative selection detection algorithm (CNSDA) and swarm negative selection detection algorithm (SNSDA). These methods were evaluated on EEG data using common measures in medical diagnosis. The findings demonstrated that the methods can efficiently achieve a reliable recognition of epileptic activity in EEG signals. Although CNSCA gave the best performance, CNSDA and SNSDA are preferred due to their efficiency in time and space. A comparison with other methods in the literature showed the competitiveness of the proposed methods

Identification of brain epileptiform discharges from electroencephalograms

Brain interictal epileptiform discharges (IEDs), as the fundamental indicators of seizure, are transient events occurring between two or before seizure onsets, captured using electroencephalogram (EEG). For epilepsy diagnosis and localization of seizure sources, both interictal and ictal recordings are extremely informative. Accurate detection of IEDs from over the scalp helps faster diagnosis of epilepsy. The scalp EEG (sEEG) suffers from a low signal-to-noise ratio and high attenuation of IEDs due to the high skull electrical impedance. On the other hand, the intracranial EEG (iEEG) recorded using implanted electrodes enjoys high temporal-spatial resolution and enables capturing most IEDs. Therefore, in this thesis, the focus is on the identification of IEDs from the concurrent scalp and intracranial EEGs. Multi-way analysis provides an opportunity to jointly analyse the data in different domains. IEDs may share some features within and between the segments. We have developed methods based on multi-way analysis and tensor factorization to detect the IEDs from the concurrent sEEG in both segmented and real-time approaches. The diversities in IED morphology, strength, and source location within the brain cause a great deal of uncertainty in their labeling by clinicians. We have exploited and incorporated this uncertainty (the probability of the waveform being an IED) in an IED detection system. Furthermore, IEDs are naturally sparse. We have benefited from the sparsity of IED waveforms in developing an algorithm to exploit sparse common features among the IED segments, referred to as sparse common feature analysis. By mapping sEEG to iEEG, the sEEG quality is improved. In this thesis, the proposed tensor factorization maps the time-frequency features of sEEG to those of iEEG to detect the IEDs from over the scalp with high sensitivity. We have concatenated time, frequency, and channel modes of iEEG recordings into a tensor. After decomposing the tensor into temporal, spectral, and spatial components, the EEG time-frequency features have been extracted and projected onto the temporal components. Furthermore, we have developed two novel algorithms based on generative adversarial networks to map the raw sEEG to iEEG. As a result of this work, the visibility of IEDs from sEEG has over 4-fold improvement. Additionally, the outcome paves the path for future research in epilepsy prediction, seizure source localisation, and modeling the brain seizure pathways

Specification and Model-driven Trace Checking of Complex Temporal Properties

Offline trace checking is a procedure used to evaluate requirement properties over a trace of recorded events. System properties verified in the context of trace checking can be specified using different specification languages and formalisms; in this thesis, we consider two classes of complex temporal properties: 1) properties defined using aggregation operators; 2) signal-based temporal properties from the Cyber Physical System (CPS) domain. The overall goal of this dissertation is to develop methods and tools for the specification and trace checking of the aforementioned classes of temporal properties, focusing on the development of scalable trace checking procedures for such properties. The main contributions of this thesis are: i) the TEMPSY-CHECK-AG model-driven approach for trace checking of temporal properties with aggregation operators, defined in the TemPsy-AG language; ii) a taxonomy covering the most common types of Signal-based Temporal Properties (SBTPs) in the CPS domain; iii) SB-TemPsy, a trace-checking approach for SBTPs that strikes a good balance in industrial contexts in terms of efficiency of the trace checking procedure and coverage of the most important types of properties in CPS domains. SB-TemPsy includes: 1) SB-TemPsy-DSL, a DSL that allows the specification of the types of SBTPs identified in the aforementioned taxonomy, and 2) an efficient trace-checking procedure, implemented in a prototype tool called SB-TemPsy-Check; iv) TD-SB-TemPsy-Report, a model-driven trace diagnostics approach for SBTPs expressed in SB-TemPsy-DSL. TD-SB-TemPsy-Report relies on a set of diagnostics patterns, i.e., undesired signal behaviors that might lead to property violations. To provide relevant and detailed information about the cause of a property violation, TD-SB-TemPsy-Report determines the diagnostics information specific to each type of diagnostics pattern. Our technological contributions rely on model-driven approaches for trace checking and trace diagnostics. Such approaches consist in reducing the problem of checking (respectively, determining the diagnostics information of) a property over an execution trace to the problem of evaluating an OCL (Object Constraint Language) constraint (semantically equivalent to ) on an instance (equivalent to ) of a meta-model of the trace. The results — in terms of efficiency of our model-driven tools—presented in this thesis are in line with those presented in previous work, and confirm that model-driven technologies can lead to the development of tools that exhibit good performance from a practical standpoint, also when applied in industrial contexts

Automated system for detection of epileptiform patterns in EEG by using a modified RBFN classifier

WOS: 000230947400023In this paper, we present a two-stage system based on a modified radial basis function network (RBFN) classifier for an automated detection of epileptiforrn pattern (EP) in an electroencephalographic signal. In the first stage, a discrete perceptron fed by six features are used to classify the peaks into two subgroups: (i) definite non-EPs and (ii) definite EPs and EP-like non-EPs. In the second stage, the peaks falling into the second group are aimed to be separated from each other by a modified RBFN designed by a perturbation method that would function as a post-classifier. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the RBFN output corresponding to the perturbed inputs. Thus, input dimension size is reduced and network becomes smaller. The classification performance of the system is comparatively evaluated for three different feature sets such as raw EEG data, discrete Fourier transform coefficients, and discrete wavelet transform coefficients. (C) 2005 Elsevier Ltd. All rights reserved