EEG Monitoring System Research Articles

An IoT-Based Novel Hybrid Seizure Detection Approach for Epileptic Monitoring

This research focuses on a new Electroencephalogram (EEG)-based system for the early detection of epileptic episodes that is made possible by the Internet of Things (IoT). The system is made up of two important units, namely, 1) the multichannel EEG recording unit; and 2) the seizure detection unit. Since epileptic information is more useful when included in multichannel EEG data from many brain regions, the primary goals of this work are: designing and developing the seizure detection unit, making use of Spike-Statistical (SS)- Flower Pollination Algorithm (FPA) based Critical Spectral Verge (CSV) derived features termed as SS-CSV; and employing the convolutional neural network (CNN) method in an IoT-enabled EEG monitoring system to detect EEG seizures. The presented system performed better, with an average accuracy of 98.48% with the CNN classifier. Neuroexperts will find this approach very useful to analyse seizure information, especially in wearable medical devices.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy

BackgroundRecent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PurposeThe study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. Materials and methodsAdults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. ResultsTwelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. ConclusionsEEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Is the Patient State Index a reliable parameter as guide to anaesthesiology in cranial neurosurgery? A first intraoperative study and a literature review

BackgroundPatient State Index (PSI) and Suppression Ratio (SR) are two indices calculated by quantitative analysis of EEG used to estimate the depth of anaesthesia but their validation in neurosurgery must be done. Our aim was to investigate the congruity PSI and SR with raw EEG monitoring in neurosurgery. MethodsWe included 34 patients undergoing elective cranial neurosurgery. Each patient was monitored by a SedLine device (PSI and SR) and by raw EEG. To appraise the agreement between PSI, SR and EEG Suppr%, Bland-Altman analysis was used. We also correlated the PSI and SR recorded at different times during surgery to the degree of suppression of the raw EEG data by Spearman's rank correlation coefficient. For a comparison with previous data we made an international literature review according to PRISMA protocol. ResultsAt all recording times, we found that there is a strong agreement between PSI and raw EEG. We also found a significant correlation for both PSI and SR with the EEG suppression percentage (p < 0.05), but with a broad dispersion of the individual values within the confidence interval. ConclusionThe Masimo SedLine processed EEG monitoring system can be used as a guide in the anaesthetic management of patients during elective cranial neurosurgery, but the anaesthesiologist must be aware that previous correlations between PSI and SR with the suppression percentage may not always be valid in all individual patients. The use of an extended visual raw EEG evaluated by an expert electroencephalographer might help to provide better guidance.

Electroencephalographic monitoring of anesthesia during surgical procedures in mice using a modified clinical monitoring system

Monitoring brain activity and associated physiology during the administration of general anesthesia (GA) in mice is pivotal to guarantee postanesthetic health. Clinically, electroencephalogram (EEG) monitoring is a well-established method to guide GA. There are no established methods available for monitoring EEG in mice (Mus musculus) during surgery. In this study, a minimally invasive rodent intraoperative EEG monitoring system was implemented using subdermal needle electrodes and a modified EEG-based commercial patient monitor. EEG recordings were acquired at three different isoflurane concentrations revealing that surgical concentrations of isoflurane anesthesia predominantly contained burst suppression patterns in mice. EEG suppression ratios and suppression durations showed strong positive correlations with the isoflurane concentrations. The electroencephalographic indices provided by the monitor did not support online monitoring of the anesthetic status. The online available suppression duration in the raw EEG signals during isoflurane anesthesia is a straight forward and reliable marker to assure safe, adequate and reproducible anesthesia protocols.

Continuous autonomic nervous system assessment using heart rate variability methodology during near-death and mystical experiences

Introduction: We have recently recorded, processed, and published preliminary research (Part I) on brain activity by quantitative electroencephalography (EEG) tomography (QEEGt), comparing separate subjects remembering their near-death experiences (NDEs) and mystical experiences (SCE). Several reports have affirmed that NDE and SCE are related to important functional changes in the autonomic nervous system (ANS). The autonomic nervous system and the hypothalamus regulate pulse, blood pressure, breathing, and arousal in response to emotional cues. When activated, the sympathetic nervous system prepares the body for emergency actions by controlling the glands of the endocrine system. Heart rate variability (HRV) refers to how much an individual's heart rate (HR) varies, and it is a powerful method to assess the ANS. Several reports have affirmed that NDE and SCE are related to important functional changes in the ANS. We used the continuous EEG monitoring (CEEG) system to compare the memories of two groups with a NDE and a SCE. CEEG permits continuous electrocardiogram monitoring, allowing calculation of HR and HRV during SCE and NDE remembering. Hence, using HRV methodology, it is possible to assess the emotional effect of remembering NDE and SCE. Conclusion: We demonstrated the usefulness of using the CEEG methodology, which allows us to continuously assess the ANS through the HRV methodology, showing important significant functional changes in the autonomic nervous system (ANS), and comparing SCE and NDE.

Outpatient long-term video EEG as new diagnostic approach in Germany: results of a feasibility study

In Germany, long-term video EEG as the gold standard for the diagnostics of epilepsy and other seizure disorders, is currently only available for inpatient monitoring in alimited number of specialized centers. These limited monitoring capacities and the large amount of associated time and work resources lead to asignificant waiting time for this important diagnostic procedure nationwide. New portable sensor technology and automated data analysis methods are creating opportunities for gold standard long-term video EEG assessments in outpatient settings, which may help to resolve this barrier. Here, we report the results of asingle-center feasibility study by implementing outpatient long-term video EEG (ALVEEG) as adiagnostic pathway in Germany. In the new diagnostic pathway, the use of innovative, portable video EEG monitoring systems along with artificial intelligence-assisted data analysis are intended to provide those patients affected by seizure disorders with amore rapid, efficient, and cross-sectoral access to gold standard diagnostics in the home environment. The diagnostics were well accepted by patients and clinicians and may represent acomplementary option to inpatient monitoring to eliminate current bottlenecks in diagnostics and care.

GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection

The EEG is an indispensable tool for clinical diagnosis of neurological disorders. The issue that how to correctly decoding the pathological information from EEG has been attracting a lot of interest. This paper creates a robust feature extraction scheme based on synchrosqueezing short-time Fourier transform (SSTFT) and graph regularized non-negative matrix factorization (GNMF), which is applied to epileptic EEG detection. The SSTFT is employed to analyze the EEG in time–frequency domain, followed by a GNMF decomposition that maps these components into a low-dimensional representation where latent clues are easily emphasized and learned, and finally the logarithmic energy, computed as features, is fed to a fuzzy K-Nearest Neighbor classifier for the purpose of discriminating seizure events. Our method is evaluated using two public databases including Bonn database and CHB-MIT database. Obtained results confirm the superiority of the proposed features by achieving classification accuracy of at least 98.0% for both short-term and long-term EEG recordings. It is expected to offer a new thought and perspective for the study and design of the automatic EEG monitoring system.

Wireless EEG monitoring system based on TGAM

Wireless EEG monitoring system based on TGAM

Analysis of EEG Signals Contaminated With Motion Artifacts Using Multiscale Modified-Distribution Entropy

The detection of motion artifacts in electroencephalogram (EEG) analysis is a high priority, especially for wearable, portable, or wireless EEG monitoring systems. Recently, many scholars have proposed numerous promising techniques to address this issue, e.g., blind source separation (BSS) and independent component analysis (ICA). However, real-time detection with low processing time and friendly use for embedded systems in wearable devices still needs more investigation. Therefore, in this paper, we considered an alternative method for approaching the motion artifact detection problem in EEG signals and proposed a new method called multiscale modified-distribution entropy (M-mDistEn). An efficient coarse-grained procedure was added into the modified-distribution entropy (mDistEn) to consider the various scales (frequencies) of the signals. The results showed that this method is suitable for distinguishing between noisy and normal portions of the signal with a maximum accuracy of approximately 93%. In addition, the M-mDistEn method led to $p$ -values of less than 0.05, indicating statistically significant results of differentiating the motion artifact EEG signals from the clean EEG signals. By comparing the different multiscale entropies, our proposed multiscale entropy was more accurate and robust. Thus, the proposed M-mDistEn method is an effective and efficient entropy method for the analysis of EEG signals corrupted by motion artifacts.

A modified row-sparse multiple measurement vector recovery algorithm for reconstructing multichannel EEG signals from compressive measurements

In this paper, a new method for reconstructing multichannel EEG signals from their compressive measurements is proposed which exploits the inter-channel correlation. Such correlation is used in the row-sparse multiple measurement vector (RSMMV) recovery approach to improve its performance in reconstructing multichannel EEGs. In this approach, it is assumed that the multichannel EEG signal ensemble is a row-sparse matrix in some domain such as wavelet, discrete cosine transform (DCT), or Gabor. However, this assumption does not hold in practice for real EEG signals. In order to overcome this limitation, a modified RSMMV algorithm is proposed which exploits sub-matrices of a given EEG matrix for which the assumption of row-sparsity is satisfied. The proposed method is applied to 2 multichannel EEG datasets chosen from the BCI competition III and the OSF databases and its performance is evaluated and compared with that of the RSMMV recovery approach; which has been proven to be superior to well-known single-channel recovery methods. Experimental results show that, in comparison with the RSMMV recovery approach, the proposed method achieves improvements of up to 4% and 2% in the reconstruction accuracy measured by the normalized mean squared error in the BCI and the OSF datasets respectively. The results also show that the proposed algorithm outperforms the blind compressed sensing method and has a comparable performance with the low-rank approach. The proposed method can therefore be deployed in wireless body area network based EEG monitoring systems.

Design and Implementation of Brain-Apparatus Conversations Portable EEG Monitoring System based on OpenBCI

Design and Implementation of Brain-Apparatus Conversations Portable EEG Monitoring System based on OpenBCI

FPGA Based Architecture Implementation for Epileptic Seizure Detection Using One Way ANOVA and Genetic Algorithm.

Epilepsy is a brain disorder which produces recurrent seizures as a storm of the electrical activity of the brain. 70 millions of people living with epilepsy in the world and most of them are from developing countries and near about 12 millions of people are residing from India. In rural areas, seizure disorder is not treated seriously so there is a need for awareness and availability of proper medication. Recurring seizures are the major source of diagnosis of epilepsy so real-time prediction using analytical methods is a need of the research in this area. Electroencephalographic (EEG) signals are the rich source of the early diagnosis of epilepsy. The basic objective of the work is to proposed real time architecture which could be included in existing EEG monitoring and measuring instruments to mark the seizure occurrence. This will facilitate medical practitioners monitoring primary status of patients and understanding frequency of seizure occurrence. Thus the proposed work provide real-time architecture or improved performance reconfigurable solution to contribute in designing real-time seizure detection system. The EEG processing architecture is designed and implemented in this work, which will add values to the existing EEG monitoring and recording system.

Analysis of a Low-Cost EEG Monitoring System and Dry Electrodes toward Clinical Use in the Neonatal ICU.

Electroencephalography (EEG) is an important clinical tool for monitoring neurological health. However, the required equipment, expertise, and patient preparation inhibits its use outside of tertiary care. Non-experts struggle to obtain high-quality EEG due to its low amplitude and artefact susceptibility. Wet electrodes are currently used, which require abrasive/conductive gels to reduce skin-electrode impedance. Advances in dry electrodes, which do not require gels, have simplified this process. However, the assessment of dry electrodes on neonates is limited due to health and safety barriers. This study presents a simulation framework for assessing the quality of EEG systems using a neonatal EEG database, without the use of human participants. The framework is used to evaluate a low-cost EEG acquisition system and compare performance of wet and dry (Micro Transdermal Interface Platforms (MicroTIPs), g.tec-g.SAHARA) electrodes using accurately acquired impedance models. A separate experiment assessing the electrodes on adult participants was conducted to verify the simulation framework’s efficacy. Dry electrodes have higher impedance than wet electrodes, causing a reduction in signal quality. However, MicroTIPs perform comparably to wet electrodes at the frontal region and g.tec-g.SAHARA performs well at the occipital region. Using the simulation framework, a 25dB signal-to-noise ratio (SNR) was obtained for the low-cost EEG system. The tests on adults closely matched the simulated results.

System Level Framework for Assessing the Accuracy of Neonatal EEG Acquisition.

Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on a vulnerable population such as newborns is complicated due to ethical and regulatory considerations that slow down the technical development. This paper presents and validates a method for quantifying the accuracy of neonatal EEG acquisition systems and electrode technologies via clinical data simulations that do not require neonatal participants. The proposed method uses an extensive neonatal EEG database to simulate analogue signals, which are subsequently passed through electrical models of the skin-electrode interface, which are developed using wet and dry EEG electrode designs. The signal losses in the system are quantified at each stage of the acquisition process for electrode and acquisition board losses. SNR, correlation and noise values were calculated. The results verify that low-cost EEG acquisition systems are capable of obtaining clinical grade EEG. Although dry electrodes result in a significant increase in the skin-electrode impedance, accurate EEG recordings are still achievable.

Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring

Long-term electroencephalogram (EEG) monitoring is effective for epilepsy diagnosis. However, it also takes a lot of time for clinicians to correctly interpret the long-time recordings. Real-time computer-based EEG monitoring and classification systems have attracted recently the attention of researchers to help clinicians locate online possible epileptic-form EEG signals. In this paper, we first present an accurate and fast EEG classification algorithm that can recognize three types of EEG signals: normal, spike, and seizure. 16-channel bipolar EEG recordings of epilepsy patients are preprocessed, segmented, and ensemble empirical mode decomposed (EEMD) into intrinsic mode functions (IMFs). Features are extracted and linear discriminant analysis (LDA) is applied to train two classifiers: one is for seizure and non-seizure discrimination, and the other is for normal and spike discrimination. In order to furthermore help the clinicians, the results of LDA are visualized and sonified. The changes of the discriminant in the LDA on continuous EEG segments are backtracked to each feature, and thus to each EEG channel. Accordingly, contours of the changes in EEG channels are depicted. At the same time, sinusoidal waves in 440 or 880 Hz are played when EEG segments are classified into spike or seizure respectively. In the experiment, EEG recordings of six subjects (two normal and four seizure patients) are examined. The experiment result shows that the accuracy of the proposed epileptic EEG classification algorithm is relatively high. In addition, the visualization and sonification algorithms of epileptic-form EEG may greatly help clinicians localize the focus of seizure and nurses take care of seizure patients, immediately.

Demonstration of long-distance hazard-free wearable EEG monitoring system using mobile phone visible light communication.

A wearable electroencephalogram (EEG) is a small mobile device used for long-term brain monitoring systems. Applications of these systems include fatigue monitoring, mental/emotional monitoring, and brain-computer interfaces. However, the usage of wireless wearable EEG systems is limited due to the risks posed by the wireless RF communication radiation in a long-term exposure to the human brain. A novel microwave radiation-free system was developed by integrating visible light communication technology into a wearable EEG device. In this work, we investigated the system's performance in transmitting EEG data at different illuminance level and proposed an algorithm that functions at low illuminance levels for increased transmission distance. Using a 30 Hz smartphone camera, the proposed system was able to transmit 2.4 kbps of error-free EEG data up to 4 meter, which is equal to ~300 lux using an aspheric focus lens.

Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method.

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4% can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

An automatic mobile-health based approach for EEG epileptic seizures detection

In this article, we develop a comprehensive mobile-based approach, which is able to perform the essential processes needed to automatically analyze and detect epileptic seizures using the information contained in electroencephalography (EEG) signals. We first develop and implement an appropriate combination of different algorithms that resample, smooth, remove artifacts, and constantly and adaptively segment signals to prepare them for further processing. We then improve and fully implement a large variety of features introduced in the literature of epileptic seizures detection. We also select the relevant features to reduce a feature vector space and improve the classification process by developing two automated filter and wrapper selection algorithms. We thoroughly compare between these selection algorithms in terms of redundant features, execution time and classification accuracy through three experiments. We subsequently exploit the selected features as input to a machine learning classifier to detect epileptic seizure states in a reasonable time. We experimentally and theoretically evaluate the scalability of the whole algorithm respectively on patients’ data available in standard clinical database and on 500 EEG recordings including 500 seizures. Having efficient and scabble algorithm, we develop two extra algorithms to dynamically acquire and transmit EEG signals from wireless sensors attached to patients and to visualize on mobile devices the obtained processing and analysis results. We finally integrate all our algorithms together along with an android mobile application to implement an effective mobile-based EEG monitoring system where its accuracy is tested on live EEG data.

Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network

Recent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless electroencephalography (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time healthcare applications. One master consideration in using such battery-powered wireless EEG monitoring system is energy constraint at the sensor side. The traditional EEG streaming approach imposes an excessive power consumption, as it transmits the entire EEG signals wirelessly. Therefore, innovative solutions to alleviate the total power consumption at the receiver are highly desired. This work introduces the use of the discrete wavelet transform and compressive sensing algorithms for scalable EEG data compression in wireless sensors in order to address the power and distortion constraints. Encoding and transmission power models of both systems are presented which enable analysis of power and performance costs. We then present a theoretical analysis of the obtained distortion caused by source encoding and channel errors. Based on this analysis, we develop an optimization scheme that minimizes the total distortion for different channel conditions and encoder settings. Using the developed framework, the encoder can adaptively tune the encoding parameters to match the energy constraint without performance degradation.

A Cauchy-Based State-Space Model for Seizure Detection in EEG Monitoring Systems

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0.08 per hour and a median time delay of 8.10 seconds, demonstrating the method's effectiveness.