scholarly journals Variations in Values of State, Response Entropy and Haemodynamic Parameters Associated with Development of Different Epileptiform Patterns during Volatile Induction of General Anaesthesia with Two Different Anaesthetic Regimens Using Sevoflurane in Comparison with Intravenous Induct: A Comparative Study

2020 ◽  
Vol 10 (6) ◽  
pp. 366
Author(s):  
Michał Stasiowski ◽  
Anna Duława ◽  
Izabela Szumera ◽  
Radosław Marciniak ◽  
Ewa Niewiadomska ◽  
...  
Keyword(s):  
General Anaesthesia ◽  
Prospective Clinical Study ◽  
Eeg Signals ◽  
Minimal Alveolar Concentration ◽  
Facial Electromyography ◽  
Epileptiform Discharges ◽  
Eeg Data ◽  
Response Entropy ◽  
End Tidal ◽  
Volatile Induction

Background and Objectives: Raw electroencephalographic (EEG) signals are rarely used to monitor the depth of volatile induction of general anaesthesia (VIGA) with sevoflurane, even though EEG-based indices may show aberrant values. We aimed to identify whether response (RE) and state entropy (SE) variations reliably reflect the actual depth of general anaesthesia in the presence of different types of epileptiform patterns (EPs) in EEGs during induction of general anaesthesia. Materials and Methods: A randomized, prospective clinical study was performed with 60 patients receiving VIGA using sevoflurane with the increasing concentrations (group VIMA) or the vital capacity (group VCRII) technique or an intravenous single dose of propofol (group PROP). Facial electromyography (fEMG), fraction of inspired sevoflurane (FiAA), fraction of expired sevoflurane (FeAA), minimal alveolar concentration (MAC) of sevoflurane, RE and SE, and standard electroencephalographic evaluations were performed in these patients. Results: In contrast to periodic epileptiform discharges, erroneous SE and RE values in the patients’ EEGs were associated with the presence of polyspikes (PS) and rhythmic polyspikes (PSR), which were more likely to indicate toxic depth rather than false emergence from anaesthesia with no changes in the FiAA, FeAA, and MAC of sevoflurane. Conclusion: Calculated RE and SE values may be misleading during VIGA when EPs are present in patients’ EEGs. During VIGA with sevoflurane, we recommend monitoring raw EEG data in scientific studies to correlate it with potentially erroneous RE and SE values and the end-tidal concentration of sevoflurane in everyday clinical practice, when monitoring raw EEG is not available, because they can mislead anaesthesiologists to reduce sevoflurane levels in the ventilation gas and result in unintentional true emergence from anaesthesia. Further studies are required to investigate the behaviour of EEG-based indices during rapid changes in sevoflurane concentrations at different stages of VIGA and the influence of polyspikes and rhythmic polyspikes on the transformation of EEG signals into a digital form.

Polyspikes and Rhythmic Polyspikes During Volatile Induction of General Anesthesia With Sevoflurane Result in Bispectral Index Variations

2020 ◽  
pp. 155005942097457
Author(s):  
Michał J. Stasiowski ◽  
Anna Duława ◽  
Seweryn Król ◽  
Radosław Marciniak ◽  
Wojciech Kaspera ◽  
...  
Keyword(s):  
General Anesthesia ◽  
Bispectral Index ◽  
Vital Capacity ◽  
Current Analysis ◽  
Minimal Alveolar Concentration ◽  
Facial Electromyography ◽  
Epileptiform Discharges ◽  
Toxic Concentration ◽  
Different Types ◽  
Volatile Induction

Background Although electroencephalography (EEG)-based indices may show artifactual values, raw EEG signal is seldom used to monitor the depth of volatile induction of general anesthesia (VIGA). The current analysis aimed to identify whether bispectral index (BIS) variations reliably reflect the actual depth of general anesthesia during presence of different types of epileptiform patterns (EPs) in EEGs during induction of general anesthesia. Methods Sixty patients receiving either VIGA with sevoflurane using increasing concentrations (group VIMA) or vital capacity (group VCRII) technique or intravenous single dose of propofol (group PROP) were included. Monitoring included facial electromyography (fEMG), fraction of inspired sevoflurane (FiAA), fraction of expired sevoflurane (FeAA), minimal alveolar concentration (MAC) of sevoflurane, BIS, standard EEG, and hemodynamic parameters. Results In the PROP group no EPs were observed. During different stages of VIGA with sevoflurane in the VIMA and VCRII groups, presence of polyspikes and rhythmic polyspikes in patients’ EEGs resulted in artifactual BIS values indicating a false awareness/wakefulness from anesthesia, despite no concomitant change of FiAA, FeAA, and MAC of sevoflurane. Periodic epileptiform discharges did not result in aberrant BIS values. Conclusion Our results suggest that raw EEG correlate it with values of BIS, FiAA, FeAA, and MAC of sevoflurane during VIGA. It seems that because artifactual BIS values indicating false awareness/wakefulness as a result of presence of polyspikes and rhythmic polyspikes in patients’ EEGs may be misleading to an anesthesiologist, leading to unintentional administration of toxic concentration of sevoflurane in ventilation gas.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

Abstract W P333: Seizure as the Presenting Symptom of ICH Patient Characteristics and EEG Utilization

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Kanika Arora ◽  
Alyssa Gadpaille ◽  
Karen C. Albright ◽  
Muhammad Alvi ◽  
Ayaz Khawaja ◽  
...  
Keyword(s):  
Substance Abuse ◽  
Clinical Evidence ◽  
Patient Characteristics ◽  
Rhythmic Pattern ◽  
Epileptiform Discharges ◽  
Number Of Patients ◽  
Eeg Data ◽  
History Of ◽  
Ich Score

Background and Purpose: Seizures are the presenting symptom in a significant number of patients with spontaneous ICH. The role of EEG in the routine evaluation patients, with or without clinical evidence of seizures, is unclear. This study was undertaken to better understand seizures and the use of EEG in patients with ICH. Methods: Retrospective review of consecutive spontaneous ICH patients at our institution from 2008-2013. Patients were considered to have a seizure on presentation if a clinical evidence of a seizure was documented in the medical record; EEG data was not required to confirm seizure on presentation. Demographics, vascular risk factors, ICH score, and EEG findings were assessed. Results: Of 402 spontaneous ICH patients (mean age 63, 42% black, 43% female), 10% presented with seizure. Patients presenting with seizure were younger (mean age 65 vs. 54, p<.001). Compared to patients with ICH presenting without a seizure, blacks presented more frequently with seizure (62% vs. 40%, p=.009). A higher proportion of patients who presented with seizure had a history of alcohol use (50% vs. 27%, p=.008) and substance abuse (23% vs. 10%, p=.025). Patients who presented with seizure more frequently had cortical ICH (54% vs. 32%, p=.007). EEGs were performed more frequently in ICH patients that presented with seizure (66% vs. 19%, p<.001). Among patients with an EEG, epileptiform discharges or rhythmic pattern was more common in patients who presented with seizure (30% vs. 10%, p=.040) and with a cortical ICH (29% vs. 9%, p=.036). There were no significant differences in the proportion of patients that received EEG based on race, history of alcohol abuse, or history of substance abuse. Conclusions: Patients who presented with seizure were younger, black, and a higher proportion had a history of alcohol and substance abuse compared to patients with ICH who did not present with a seizure. Only 66% of those presenting with clinical seizure underwent EEG. Despite the prevalence of subclinical seizures in ICH patients, only 19% of patients who did not present with a seizure underwent EEG. Our study suggests that there may be room for improvement on the part of stroke neurologists in the diagnosis and management seizure of ICH patients.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

Alternative Diagnosis of Epilepsy in Children Without Epileptiform Discharges Using Deep Convolutional Neural Networks

2019 ◽  
Vol 30 (05) ◽  
pp. 1850060 ◽  
Author(s):  
Lung-Chang Lin ◽  
Chen-Sen Ouyang ◽  
Rong-Ching Wu ◽  
Rei-Cheng Yang ◽  
Ching-Tai Chiang
Keyword(s):  
Sensitivity And Specificity ◽  
Performance Measures ◽  
Diagnostic Value ◽  
Proof Of Concept ◽  
Deep Convolutional Neural Networks ◽  
Psychogenic Nonepileptic Seizures ◽  
Epileptiform Discharges ◽  
Eeg Data ◽  
Nonepileptic Seizures ◽  
Patients With Epilepsy

Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients’ lives considerably. Electroencephalography is commonly used for diagnosing epilepsy. Although on electroencephalograms (EEGs), epileptiform discharges (ED) specifically indicate epilepsy, only approximately 50% of patients with epilepsy have ED in their first EEG. In this study, we developed a deep convolutional neural network (ConvNet)-based classifier to distinguish EEG between patients with epilepsy without ED and controls. Overall, 25 patients with epilepsy without ED in their EEGs and 25 age-matched patients with Tourette syndrome or syncope were enrolled. Their EEGs were classified using the deep ConvNet. When the EEG data without overlapping were used, the accuracy, sensitivity, and specificity were 65.00%, 48.00%, and 82.00%, respectively. The performance measures improved when the input EEG data were augmented through overlapping. With 95% EEG data overlapping, the accuracy, sensitivity, and specificity increased to 80.00%, 70.00%, and 90.00%, respectively. The proposed method could be regarded as a pilot study to demonstrate a proof of concept of a potential diagnostic value of deep ConvNet in patients with epilepsy without ED. Further studies are needed to assist neurologists in distinguishing nonepileptic paroxysmal events from epilepsy.

scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

2020 ◽  
Vol 40 (3) ◽  
pp. 116-123
Author(s):  
Zoran Šverko ◽  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Data Processing ◽  
Mean Value ◽  
Adhd Group ◽  
Eeg Signal ◽  
Statistical Features ◽  
Eeg Signals ◽  
New Approach ◽  
Mean Values ◽  
Adhd Diagnosis ◽  
Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

A portable real-time EEG signal acquisition and tele-medicine system

2020 ◽  
pp. 1-11
Author(s):  
Qiang Zhang ◽  
Peng Wang ◽  
Shanshan Li ◽  
Yonghao Jing
Keyword(s):  
Real Time ◽  
Sql Server ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eeg Data ◽  
Sql Server Database ◽  
High Power Consumption ◽  
Machine Interface ◽  
Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

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