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

Accepted versio

SEMIAUTOMATIC ANALYSIS OF SLEEP MICROSTRUCTURE PARAMETERS: AROUSAL, CYCLIC ALTERNATING PATTERN AND REM MUSCLE ATONIA.

This thesis project is focused on systems of automatic analysis of sleep parameters and it is composed by two main parts: the first is focused on the process of creation of a software for the analysis of Cyclic Alternating Pattern (CAP) a particular parameter of sleep microstructure and the second part is focused on the use of automatic analysis of muscle activity during sleep. CAP is defined as periodic EEG activity of NREM sleep characterized by sequences of transient electrocortical events, that are distinct from the background electroencephalogram (EEG) activity and occurs at up to 1-minute intervals. CAP represents the microstructure of sleep, and its analysis gives fundamental information that are otherwise neglected with the analysis of sleep macrostructure (sleep staging) alone. CAP is considered a marker for the evaluation of sleep stability and its oscillatory presence is fundamental preservation of sleep stability through the night and in response to arousal stimuli. Analysis of CAP is a very time consuming procedure and it is still used mainly for research purpose rather than in the clinical practice. The development of a software for the analysis of CAP was the main focus of the work in collaboration with Micromed® (an international company for the manufacturing of hardware and software for neurophysiology based in Mogliano Veneto (TV)). During the months spent at Micromed® the PhD student worked with the software programmers and engineers for the creation and validation of the software, individuating all the clinical parameters from guidelines and verifying their correct application and the validity of the results. In the first part of this thesis all the creation process is described in detail. The second part of this thesis is focused on the automatic analysis of muscle EMG tone during both REM and NREM sleep. Muscle tone during sleep gradually diminishes throughout the different sleep stages reaching its minimum with REM muscle atonia. Evaluation of muscle tone during REM sleep is fundamental for the diagnosis of REM sleep Behavior Disorder (RBD) in which there is loss of muscle atonia during REM associated to dream enacting behavior. Muscle activity is measured in polysomnography (PSG) through the recording of different EMG channels. This activity is evaluated almost exclusively during REM sleep using a manual method of visual scoring that require high expertise is highly time consuming. A validated method developed by R. Ferri and co. allows automatic analysis of chin EMG activity through the calculation of Atonia index. Few studies evaluated muscle tone during NREM sleep, and little is known about the neurophysiology of muscle control. Manual methods would be difficult to apply to NREM sleep; the method developed by Ferri is capable to perform an analysis of muscle tone for all sleep stages. RBD is associated to neurodegenerative disorders, synucleinopathies such as Parkinson disease (PD), Multiple System Atrophy (MSA). MSA patients have a more severe loss of atonia during REM sleep compared to PD with RBD. Starting from the fortuitous observation of a prominent facial activity during NREM sleep, we decided to evaluate the facial activity recorded in vPSG in patients with PD, MSA and controls and to evaluate the muscle tone in both REM and NREM sleep using the automatic method for the calculation of atonia index. Our results showed that MSA have a more sustained muscle tone compared to healthy controls in all sleep stages and compared to PD in all NREM stages. Moreover a particular facial expression was noted to be significantly more frequent in MSA compared to PD. This results may help the differential diagnosis between PD and MSA. This is the first study to evaluate muscle tone during all sleep stages using Atonia index and this analysis may open to different perspectives for the understanding of REM behavior disorder and the mechanism underlying the control of muscle tone in NREM slee

Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness

Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and wakefulness in 1 second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD.Comment: 40 pages, 13 figures, 9 table

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Hand (Motor) Movement Imagery Classification of EEG Using Takagi-Sugeno-Kang Fuzzy-Inference Neural Network

Approximately 20 million people in the United States suffer from irreversible nerve damage and would benefit from a neuroprosthetic device modulated by a Brain-Computer Interface (BCI). These devices restore independence by replacing peripheral nervous system functions such as peripheral control. Although there are currently devices under investigation, contemporary methods fail to offer adaptability and proper signal recognition for output devices. Human anatomical differences prevent the use of a fixed model system from providing consistent classification performance among various subjects. Furthermore, notoriously noisy signals such as Electroencephalography (EEG) require complex measures for signal detection. Therefore, there remains a tremendous need to explore and improve new algorithms. This report investigates a signal-processing model that is better suited for BCI applications because it incorporates machine learning and fuzzy logic. Whereas traditional machine learning techniques utilize precise functions to map the input into the feature space, fuzzy-neuro system apply imprecise membership functions to account for uncertainty and can be updated via supervised learning. Thus, this method is better equipped to tolerate uncertainty and improve performance over time. Moreover, a variation of this algorithm used in this study has a higher convergence speed. The proposed two-stage signal-processing model consists of feature extraction and feature translation, with an emphasis on the latter. The feature extraction phase includes Blind Source Separation (BSS) and the Discrete Wavelet Transform (DWT), and the feature translation stage includes the Takagi-Sugeno-Kang Fuzzy-Neural Network (TSKFNN). Performance of the proposed model corresponds to an average classification accuracy of 79.4 % for 40 subjects, which is higher than the standard literature values, 75%, making this a superior model

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

DEVELOPMENT OF AN ACCURATE SEIZURE DETECTION SYSTEM USING RANDOM FOREST CLASSIFIER WITH ICA BASED ARTIFACT REMOVAL ON EEG DATA

Abstract The creation of a reliable artifact removal and precise epileptic seizure identification system using Seina Scalp EEG data and cutting-edge machine learning techniques is presented in this paper. Random Forest classifier used for seizure classification, and independent component analysis (ICA) is used for artifact removal. Various artifacts, such as eye blinks, muscular activity, and environmental noise, are successfully recognized and removed from the EEG signals using ICA-based artifact removal, increasing the accuracy of the analysis that comes after. A precise distinction between seizure and non-seizure segments is made possible by the Random Forest Classifier, which was created expressly to capture the spatial and temporal patterns associated with epileptic seizures. Experimental evaluation of the Seina Scalp EEG Data demonstrates the excellent accuracy of our approach, achieving a 96% seizure identification rate A potential strategy for improving the accuracy and clinical utility of EEG-based epilepsy diagnosis is the merging of modern signal processing methods and deep learning algorithms