A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation

EKSTRAKSI CIRI EPILEPSI PADA REKAMAN EEG BERDASARKAN CIRI RATA-RATA, STANDARDEVIASI, MINIMAL DAN KURTOSIS

Epilepsi sering dihubungkan dengan disabilitas fisik, disabilitas mental, dan konsekuensi psikososial yang berat bagi penyandangnya. Penyakit epilepsi yang tidak ditangani segera dapat merusak otak. EEG digunakan untuk merekam otak dan membedakan pola sinyal EEG epilepsi dan normal diperlukan suatu ciri untuk masing masing pola. Ciri yang akan dipakai untuk mewakili sinyal EEG berasal dari 4 ciri statistik yaitu rata-rata, standar deviasi, minimal, kurtosis dengan 11 elektroda FP1, FP2, F7, F3, T7,T8 , Pz, O1, O2, P3, P4. Hasil menunjukkan nilai standar deviasi pada penyandang epilepsi lebih tinggi dari pada ciri rata- rata, minimal  dan kurtosi

NOVEL GRAPHICAL MODEL AND NEURAL NETWORK FRAMEWORKS FOR AUTOMATED SEIZURE DETECTION, TRACKING, AND LOCALIZATION IN FOCAL EPILEPSY

Epilepsy is a heterogenous neurological disorder characterized by recurring and unprovoked seizures. It is estimated that 60% of epilepsy patients suffer from focal epilepsy, where seizures originate from one or more discrete locations within the brain. After onset, focal seizure activity spreads, involving more regions in the cortex. Diagnosis and therapeutic planning for patients with focal epilepsy crucially depends on being able to detect epileptic activity as it starts and localize its origin. Due to the subtlety of seizure activity and the complex spatio-temporal propagation patterns of seizure activity, detection and localization of seizure by visual inspection is time-consuming and must be done by highly trained neurologists. In this thesis, we detail modeling approaches to identify and capture the spatio-temporal ictal propagation of focal epileptic seizures. Through novel multi-scale frameworks, information fusion between signal paths, and hybrid architectures, models that capture the underlying seizure propagation phenomena are developed. The first half relies on graphical modeling approaches to detect seizures and track their activity through the space of EEG electrodes. A coupled hidden Markov model approach to seizure propagation is described. This model is subsequently improved through the addition of convolutional neural network based likelihood functions, removing the reliance on hand designed feature extraction. Through the inclusion of a hierarchical switching chain and localization variables, the model is revised to capture multi-scale seizure onset and spreading information. In the second half of this thesis, end-to-end neural network architectures for seizure detection and localization are developed. First, combination convolutional and recurrent neural networks are used to identify seizure activity at the level of individual EEG channels. Through novel aggregation, the network is trained to recognize seizure activity, track its evolution, and coarsely localize seizure onset from lower resolution labels. Next, a multi-scale network capable of analyzing the global and electrode level signals is developed for challenging task of end-to-end seizure localization. Onset location maps are defined for each patient and an ensemble of weakly supervised loss functions are used in a multi-task learning framework to train the architecture

Collaborative adaptive filtering for machine learning

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data

Machine Learning for Understanding Focal Epilepsy

The study of neural dysfunctions requires strong prior knowledge on brain physiology combined with expertise on data analysis, signal processing, and machine learning. One of the unsolved issues regarding epilepsy consists in the localization of pathological brain areas causing seizures. Nowadays the analysis of neural activity conducted with this goal still relies on visual inspection by clinicians and is therefore subjected to human error, possibly leading to negative surgical outcome. In absence of any evidence from standard clinical tests, medical experts resort to invasive electrophysiological recordings, such as stereoelectroencephalography to assess the pathological areas. This data is high dimensional, it could suffer from spatial and temporal correlation, as well as be affected by high variability across the population. These aspects make the automatization attempt extremely challenging. In this context, this thesis tackles the problem of characterizing drug resistant focal epilepsy. This work proposes methods to analyze the intracranial electrophysiological recordings during the interictal state, leveraging on the presurgical assessment of the pathological areas. The first contribution of the thesis consists in the design of a support tool for the identification of epileptic zones. This method relies on the multi-decomposition of the signal and similarity metrics. We built personalized models which share common usage of features across patients. The second main contribution aims at understanding if there are particular frequency bands related to the epileptic areas and if it is worthy to focus on shorter periods of time. Here we leverage on the post-surgical outcome deriving from the Engel classification. The last contribution focuses on the characterization of short patterns of activity at specific frequencies. We argue that this effort could be helpful in the clinical routine and at the same time provides useful insight for the understanding of focal epilepsy

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain–computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition

Genetic analysis of human absence epilepsy

Idiopathic Mendelian epilepsies have been typically identified as channelopathies. Evidence suggests that mutations in genes encoding GABAA receptors, GABAB receptors or voltage-dependent calcium channels (VDCCs) may underlie childhood absence epilepsy (CAE), an idiopathic generalised epilepsy with complex inheritance. The aims of this project were: i) Ascertainment of a patient resource ii) Investigation of candidate genes by linkage analysis iii) Mutation analysis by direct sequencing iv) Construction of single nucleotide polymorphism (SNP) based haplotypes in candidate genes v) Intra-familial association analysis using SNP based haplotypes DNA and clinical data were obtained from: 53 nuclear CAE pedigrees; 29 families including individuals with CAE and a broader „absence‟ epilepsy phenotype; 217 parent-child trios; a North American family in which absence epilepsy segregates with episodic ataxia type 2 (EA2) Sixteen calcium channel genes and seven GABAA and two GABAB receptor subunit genes were excluded by linkage analysis. Significant linkage was demonstrated for CACNG3 on chromosome 16p12-p13.1 for both CAE and the broader absence phenotype. Positive linkage was also obtained at the GABRA5, GABRB3, GABRG3 cluster on chromosome 15q11-q13. Non-parametric linkage analysis was significant at both the 16p and 15q loci. Two-locus analysis supported a digenic effect from these two loci. Sequencing of CACNG3 revealed 34 sequence variants, none clearly causal, although bioinformatic analysis provided supportive functional evidence. Association analysis showed significant transmission disequilibrium both for individual single nucleotide polymorphisms (SNPs) and SNP based haplotypes spanning CACNG3. This work has provided genetic evidence that CACNG3 and at least one of the three GABAA receptor genes are susceptibility loci for absence epilepsy. Linkage analysis performed in the family with absence epilepsy and EA2 was suggestive that the VDCC CACNA1A was the causative gene. This was subsequently confirmed by sequence analysis in collaboration with the Institute of Neurology, UCL. This is the first reported family in which a CACNA1A mutation that impairs calcium channel function cosegregates with typical absence seizures and 3Hz spike-wave discharges on EEG

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosi

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware