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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals
While Deep Learning (DL) is often considered the state-of-the art for
Artificial Intelligence-based medical decision support, it remains sparsely
implemented in clinical practice and poorly trusted by clinicians due to
insufficient interpretability of neural network models. We have tackled this
issue by developing interpretable DL models in the context of online detection
of epileptic seizure, based on EEG signal. This has conditioned the preparation
of the input signals, the network architecture, and the post-processing of the
output in line with the domain knowledge. Specifically, we focused the
discussion on three main aspects: 1) how to aggregate the classification
results on signal segments provided by the DL model into a larger time scale,
at the seizure-level; 2) what are the relevant frequency patterns learned in
the first convolutional layer of different models, and their relation with the
delta, theta, alpha, beta and gamma frequency bands on which the visual
interpretation of EEG is based; and 3) the identification of the signal
waveforms with larger contribution towards the ictal class, according to the
activation differences highlighted using the DeepLIFT method. Results show that
the kernel size in the first layer determines the interpretability of the
extracted features and the sensitivity of the trained models, even though the
final performance is very similar after post-processing. Also, we found that
amplitude is the main feature leading to an ictal prediction, suggesting that a
larger patient population would be required to learn more complex frequency
patterns. Still, our methodology was successfully able to generalize patient
inter-variability for the majority of the studied population with a
classification F1-score of 0.873 and detecting 90% of the seizures.Comment: 28 pages, 11 figures, 12 table
Deep learning approach for epileptic seizure detection
Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper.
Deep learning methods were applied to a large publicly available dataset, the Childrenâs Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3â4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D.
The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data.
VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syvÀoppimisella lÀhestymistavalla. TiivistelmÀ. Epilepsia on yleisin aivosairaus, joka Maailman terveysjÀrjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikÀ on virhealtista ja aikaa vievÀÀ. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vÀhentÀÀ diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmÀt tukeutuvat pÀÀasiassa piirteisiin, jotka asiantuntijat ovat mÀÀritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta mÀÀrÀstÀ EEG-dataa. LisÀksi, EEG on epÀstationÀÀrinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten vÀlillÀ ja EEG-data sisÀltÀÀ aina useita kohinatyyppejÀ, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. NÀihin haasteisiin vastaamiseksi tÀssÀ diplomityössÀ tarkastellaan soveltuvatko syvÀoppivat menetelmÀt epilepsian havaitsemiseen EEG-tallenteista.
Aineistona kĂ€ytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). TĂ€mĂ€n työn tutkimus sisĂ€ltÀÀ kolme koeryhmÀÀ, jotka eroavat toisistaan esikĂ€sittelyvaiheiden osalta: aikasarja-EEG-data esikĂ€siteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja nĂ€in esikĂ€sitelty signaali segmentoitiin epookkeihin. Kukin koeryhmĂ€ sisĂ€ltÀÀ 3â4 koetta, jotka eroavat menetelmiltÀÀn ja tavoitteiltaan. Kussakin niistĂ€ epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. TĂ€ssĂ€ tutkimuksessa tutkittiin ja verrattiin keskenÀÀn EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriĂ€. NĂ€in saatuja aika- ja taajuusalueen signaaleja kĂ€ytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina.
Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tÀllöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstÀ riippuvaisella EEG-datalla.
VGG yhdessÀ amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstÀ riippumattoman luokittelijan opettamiseksi
Wearable electroencephalography for long-term monitoring and diagnostic purposes
Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG
units, which are the current standard for long-term EEG monitoring. Replacing
these short lifetime, bulky units with long-lasting, miniature and wearable devices
that can be easily worn by patients will result in more EEG data being collected for
extended monitoring periods. This thesis presents three new fabricated systems, in
the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of
epilepsy and sleep disorders by detecting specific clinically important EEG events
on the sensor node, while discarding background activity. The power consumption
of the WEEG monitoring device incorporating these systems can be reduced since
the transmitter, which is the dominating element in terms of power consumption,
will only become active based on the output of these systems.
Candidate interictal activity is identified by the developed analog-based interictal
spike selection system-on-chip (SoC), using an approximation of the Continuous
Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike
selection SoC is fabricated in a 0.35 ÎŒm CMOS process and consumes 950 nW.
Experimental results reveal that the SoC is able to identify 87% of interictal spikes
correctly while only transmitting 45% of the data.
Sections of EEG data containing likely ictal activity are detected by an analog
seizure selection SoC using the low complexity line length feature. This SoC is
fabricated in a 0.18 ÎŒm CMOS technology and consumes 1.14 ÎŒW. Based on experimental
results, the fabricated SoC is able to correctly detect 83% of seizure
episodes while transmitting 52% of the overall EEG data.
A single-channel analog-based sleep spindle detection SoC is developed to aid
the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic
events of sleep. The system identifies spindle events by monitoring abrupt changes
in the input EEG. An approximation of the median frequency calculation, incorporated
as part of the system, allows for non-spindle activity incorrectly identified
by the system as sleep spindles to be discarded. The sleep spindle detection SoC
is fabricated in a 0.18 ÎŒm CMOS technology, consuming only 515 nW. The SoC
achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces
Identification of the Seizure Onset Zone by Auto-Regressive Model Residual Modulation Applied to Intracranial EEG and its Correlation to Channels with High Preponderances of Detected HFOs
The objective of this thesis was to examine the ability of the Autoregressive Model Residual Modulation (ARRm) method to identify the Seizure Onset Zone (SOZ) in intracranial electroencephalogram (iEEG) of patients with refractory epilepsy. Patients who have not become seizure free after multiple trials of antiepileptic drugs (AEDs) may seek treatment through epilepsy surgery. Cortical electrodes are implanted directly on the cerebral cortex, then iEEG is collected. A specialized neurologist reviews the iEEG, then in consultation with the neurosurgeon, the SOZ is determined and areas of the brain may be chosen for resection. The success rate of epilepsy surgery varies, so it is apparent that identifying exactly where to resect epileptic tissue is still very challenging.
In recent research, High Frequency Oscillations (HFOs) in iEEG have shown strong relations to epileptic tissue. Automated HFO detection methods have been developed, but most involve analysis in the frequency domain and are computationally expensive. The ARRm method is implemented in the time domain and has potential to be implemented for real -time analysis. AR modeling is used to predict the iEEG and should not be able to accurately model highly nonharmonic events such as HFOs. Using a coefficient of variation involving the residuals of the model (ARRm value), interpretation of results showed that high ARRm values also corresponded to channel locations with high HFO counts, which included channels of interest identified by the epileptologist. Statistically significant (p\u3c0.01) correlations were drawn between the ARRm value and HFO counts on channels of interest. Examination of the AR model residual during epileptogenic events revealed that the residual was highest when spikes and/or Fast Ripples occurred. These findings suggest that significantly correlated channels may indicate the presence of fast ripples or spikes occurring with fast ripples in the signal
Epilepsy
With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well
UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS
Quality of life for the more than 15 million people with drug-resistant epilepsy is tied to how precisely the brain areas responsible for generating their seizures can be localized. High-frequency (100-500 Hz) field-potential oscillations (HFOs) are emerging as a candidate biomarker for epileptogenic networks, but quantitative HFO studies are hampered by selection bias arising out of the need to reduce large volumes of data in the absence of capable automated processing methods. In this thesis, I introduce and evaluate an algorithm for the automatic detection and classification of HFOs that can be deployed without human intervention across long, continuous data records from large numbers of patients. I then use the algorithm in analyzing unique macro- and microelectrode intracranial electroencephalographic recordings from human neocortical epilepsy patients and controls. A central finding is that one class of HFOs discovered by the algorithm (median bandpassed spectral centroid ~140 Hz) is more prevalent in the seizure onset zone than outside. The outcomes of this work add to our understanding of epileptogenic networks and are suitable for near-term translation into improved surgical and device-based treatments
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