Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal ...electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNN) and transfer learning by making use of the Temple University Hospital EEG corpus. The objective of our study is to perform a multi-class classification of epileptic seizure type, which includes simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic–clonic, and non-seizures. The 19 channels EEG time series was converted into a spectrogram stack before feeding as input to CNN. The following two different modalities were proposed using CNN: (1) Transfer learning using pretrained network, (2) Extract image features using pretrained network and classify using the support vector machine classifier. The following ten pretrained networks were used to identify the optimal network for the proposed study: Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Densenet201, Resnet18, Resnet50, and Resnet101. The highest classification accuracy of 82.85% (using Googlenet) and 88.30% (using Inceptionv3) was achieved using transfer learning and extract image features approach respectively. Comparison results showed that CNN based approach outperformed conventional feature and clustering based approaches. It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.
•We propose a CNN and transfer learning-based 8-class seizure type classification using the Temple University Hospital EEG corpus.•We propose a CNN and transfer learning-based 8-class seizure type classification•ReLU layer showed the highest accuracy using extract image feature approach•The highest accuracy of 82.85% and 88.30% was achieved using Googlenet and Inceptionv3 respectively•Extract image features approach outperformed transfer learning approach
•Matrix determinant was shown as a novel feature for seizure detection.•In total, eleven classification problems were evaluated using matrix determinant.•Descriptive analysis, histogram in polar ...coordinate and bivariate plot analysis was performed.•Classification accuracy of 99.40% was attained using matrix determinant as a feature.
Objective: An epileptic seizure is recognized as a neurological disorder caused by transient and unexpected disturbance resulting from the excessive synchronous activity of the neurons in the brain. Analysis of epileptic seizures derived from long-term recordings of electroencephalogram (EEG) is cumbersome and time consuming for a neurologist. Therefore, introducing an automated detection system surrogate the neurologist involvement all time and speed up the treatment procedure.
This study introduces a matrix determinant of EEG as a significant feature for recognition of epileptic seizures. Initially, artifact-free filtered EEG time series was arranged sequentially to form a square matrix of order, namely 13, 16, 23, and 32 and determinant was estimated. Assumed that the total elements in the square matrix represent a typical segmentation length. The experiment was conducted using EEG database obtained from the University of Bonn and Ramaiah Medical College and Hospital (RMCH). In total, eleven classification problems among non-epileptic group and epileptic EEG were composed to examine temporal dynamics of brain activity in different states of the epileptic activity. Next, the extracted feature was classified using support vector machine (SVM), K-nearest neighbor (K-NN), multi-layer perceptron (MLP) classifiers with 10-fold cross-validation.
Experimental results revealed the highest classification accuracy of 99.45% (using University of Bonn) and 97.56% (using RMCH). between normal and epileptic EEG. In addition, other classification problems and matrix orders showed better results using all the classifiers. Further, descriptive analysis, histogram plot in polar coordinates and the bivariate histogram analysis was performed. In conclusion, matrix determinant found to be a potential biomarker for the real-time detection of epileptic seizure with minimal computational complexity.
Summary We did a systematic review to address the added value of intraoperative MRI (iMRI)-guided resection of glioblastoma multiforme compared with conventional neuronavigation-guided resection, ...with respect to extent of tumour resection (EOTR), quality of life, and survival. 12 non-randomised cohort studies matched all selection criteria and were used for qualitative synthesis. Most of the studies included descriptive statistics of patient populations of mixed pathology, and iMRI systems of varying field strengths between 0·15 and 1·5 Tesla. Most studies provided information on EOTR, but did not always mention how iMRI affected the surgical strategy. Only a few studies included information on quality of life or survival for subpopulations with glioblastoma multiforme or high-grade glioma. Several limitations and sources of bias were apparent, which affected the conclusions drawn and might have led to overestimation of the added value of iMRI-guided surgery for resection of glioblastoma multiforme. Based on the available literature, there is, at best, level 2 evidence that iMRI-guided surgery is more effective than conventional neuronavigation-guided surgery in increasing EOTR, enhancing quality of life, or prolonging survival after resection of glioblastoma multiforme.
Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of ...epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.
The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. ...Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.
Display omitted
•DWT based sigmoid entropy was proposed for epileptic seizure detection.•Five different mother wavelets have been studied on three EEG databases.•Classification was performed using both segment and event based approaches.•Bio3.1, Rbio3.1, and Haar wavelets were found to be best choice for seizure detection.•The Kappa coefficients obtained for all the databases were found to be either good or very good agreement category.
Background: Augmented reality (AR) has great potential for improving image-guided neurosurgical procedures, but until recently, hardware was mostly custom-made and difficult to distribute. Currently, ...commercially available low-cost AR devices offer great potential for neurosurgery, but reports on technical feasibility are lacking. The goal of this pilot study is to evaluate the feasibility of using a low-cost commercially available head-mounted holographic AR device (the Microsoft Hololens) in the operating room. The Hololens is operated by performing specific hand gestures, which are recognized by the built-in camera of the device. This would allow the neurosurgeon to control the device "touch free" even while wearing a sterile surgical outfit.
Methods: The Hololens was tested in an operating room under two lighting conditions (general background theatre lighting only; and general background theatre lighting and operating lights) and wearing different surgical gloves (both bright and dark). All required hand gestures were performed, and voice recognition was evaluated against background noise consisting of two nurses talking at conversational speech level.
Results: Wearing comfort was sufficient, with and without regular glasses. All gestures were correctly classified regardless of lighting conditions or the sort of sterile gloves. Voice recognition was good. The visibility of the holograms was good if the device was configured to use high brightness for display.
Conclusions: We demonstrate that using a commercially available low-cost head-mounted holographic AR device is feasible in a sterile surgical setting, under different lighting conditions and using different surgical gloves. Given the availability of freely available software for application development, neurosurgery can benefit from new opportunities for image-guided surgery.
Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and ...translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.
Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These ...fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech ...directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.
PDF
