scholarly journals Implementation of High Performance EEG Based Seizure Detection And Analysis On Multicore Platform

2020 ◽  
Vol 4 (5) ◽  
pp. 5-9
Author(s):  
NAGASHYAM P ◽  
VIJAY KUMAR T
Keyword(s):  
Power Consumption ◽  
High Performance ◽  
Detection System ◽  
Multicore Processors ◽  
Seizure Detection ◽  
Extraction Process ◽  
Process Data ◽  
Detection Algorithms ◽  
Eeg Data ◽  
Two Stages

About 50 million people worldwide suffer from epilepsy, the neurological disorder characterized by seizures. The primary tool for diagnosis of an epileptic seizure is an electroencephalography (EEG) which records the brain’s spontaneous electrical activity. This requires the placement of a minimum of 16 electrodes on the scalp with each electrode being interpreted as a channel. The classification of seizure detection and analysis techniques mainly work in two stages, where features are extracted from raw EEG data in the first stage and then the obtained features are used as input for the classification process in the second stage. Traditionally the Seizure detection algorithms were implemented using DSP Processor or FPGAs. But these single core platforms are constrained with respect to speed of operation and power consumption. There is a greater need to reduce the power consumption as well to increase the speed of EEG seizure detection system. This problem can be addressed using the Multicore Processors, which process data simultaneously. This project presents a high performance multicore platform for EEG based seizure detection and analysis. This platform performs continuous multichannel detection and analysis of seizures for epilepsy patients. The detection unit will detect the seizures based on feature extraction process once the seizure detection is done enables the analysis circuit that process the data based Uridva Triyabhakyam based 128 point FFT and transmits energy and frequency contents of EEG data. All proposed blocks are simulated and synthesized using Xilinx ISE and coding is done in Verilog.

scholarly journals Implementation of High-Performance EEG Based Seizure Detection And Analysis On Multicore Platform

2020 ◽  
pp. 189-194
Author(s):  
P. Nagashyam ◽  
T. Vijay Kumar
Keyword(s):  
Power Consumption ◽  
High Performance ◽  
Detection System ◽  
Multicore Processors ◽  
Seizure Detection ◽  
Extraction Process ◽  
Process Data ◽  
Detection Algorithms ◽  
Eeg Data ◽  
Two Stages

About 50 million people worldwide su?er from epilepsy, the neurological disorder characterized by seizures. The primary tool for diagnosis of an epileptic seizure is an electroencephalography (EEG) which records the brain’s spontaneous electrical activity. This requires the placement of a minimum of 16 electrodes on the scalp with each electrode being interpreted as a channel. The classification of seizure detection and analysis techniques mainly work in two stages , where features are extracted from raw EEG data in the first stage and then the obtained features are used as input for the classification process in the second stage. Traditionally the Seizure detection algorithms were implemented using DSP Processor or FPGAs. But these single core platforms are constrained with respect to speed of operation and power consumption. There is a greater need to reduce the power consumption as well to increase the speed of EEG seizure detection system. This problem can be addressed using the Multicore Processors, which process data simultaneously. This project presents a high performance multicore platform for EEG based seizure detection and analysis. This platform performs continuous multichannel detection and analysis of seizures for epilepsy patients. The detection unit detects seizures based on feature extraction process once seizure detection is done enables the analysis circuit that process the data based Uridva Triyabhakyam based 128 point FFT and transmits energy and frequency contents of EEG data. All proposed blocks are simulated and synthesized using Xilinx ISE and coding is done in Verilog.

Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

2019 ◽  
Vol 16 (04) ◽  
pp. 1950016 ◽  
Author(s):  
Duanpo Wu ◽  
Zimeng Wang ◽  
Hong Huang ◽  
Guangsheng Wang ◽  
Junbiao Liu ◽  
...  
Keyword(s):  
Detection System ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Spike Rate ◽  
Linear Discriminant ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Eeg Data ◽  
The Family

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

Neural network based seizure detection system using raw EEG data

2016 ◽  
Author(s):  
Tianchan Guan ◽  
Xiaoyang Zeng ◽  
Letian Huang ◽  
Tianchan Guan ◽  
Mingoo Seok
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Seizure Detection ◽  
Eeg Data

scholarly journals A framework for genomic sequencing on clusters of multicore and manycore processors

2016 ◽  
Vol 32 (3) ◽  
pp. 393-406 ◽  
Author(s):  
Héctor Martínez ◽  
Sergio Barrachina ◽  
Maribel Castillo ◽  
Joaquín Tárraga ◽  
Ignacio Medina ◽  
...  
Keyword(s):  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Message Passing Interface ◽  
Multicore Processors ◽  
Global Alignment ◽  
Genomic Sequencing ◽  
Single Node ◽  
Strongly Coupled ◽  
Two Stages

The advances in genomic sequencing during the past few years have motivated the development of fast and reliable software for DNA/RNA sequencing on current high performance architectures. Most of these efforts target multicore processors, only a few can also exploit graphics processing units, and a much smaller set will run in clusters equipped with any of these multi-threaded architecture technologies. Furthermore, the examples that can be used on clusters today are all strongly coupled with a particular aligner. In this paper we introduce an alignment framework that can be leveraged to coordinately run any “single-node” aligner, taking advantage of the resources of a cluster without having to modify any portion of the original software. The key to our transparent migration lies in hiding the complexity associated with the multi-node execution (such as coordinating the processes running in the cluster nodes) inside the generic-aligner framework. Moreover, following the design and operation in our Message Passing Interface (MPI) version of HPG Aligner RNA BWT, we organize the framework into two stages in order to be able to execute different aligners in each one of them. With this configuration, for example, the first stage can ideally apply a fast aligner to accelerate the process, while the second one can be tuned to act as a refinement stage that further improves the global alignment process with little cost.

Automatic Seizure Detection using Fully Convolutional Nested LSTM

2020 ◽  
Vol 30 (04) ◽  
pp. 2050019 ◽  
Author(s):  
Yang Li ◽  
Zuyi Yu ◽  
Yang Chen ◽  
Chunfeng Yang ◽  
Yue Li ◽  
...  
Keyword(s):  
Detection Rate ◽  
Short Term Memory ◽  
Detection System ◽  
Real Life ◽  
Seizure Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Convolutional Network ◽  
Eeg Data ◽  
High Level

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44–100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB–MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

scholarly journals Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD

2020 ◽  
Vol 32 ◽  
pp. 02008
Author(s):  
Meenal Vijay Kakade ◽  
Chandrakant J. Gaikwad ◽  
Vijay R. Dahake
Keyword(s):  
Signal Processing ◽  
Video Processing ◽  
Detection System ◽  
Wavelet Packet ◽  
Seizure Detection ◽  
Detection Algorithms ◽  
Analysis Process ◽  
Improved Performance ◽  
Eeg Recordings ◽  
Processing Techniques

The use of computer aided diagnosis systems for disease identifiscation, based on signal processing, image processing and video processing terminologies is common due to emerging technologies in medical field. The detection of epilepsy seizures using EEG recordings is done by different signal processing techniques. To reduce the disability caused by the uncertainty of the occurrence of seizures, a recording system which shall result accurate and early detection of seizure with quick warning is greatly desired. To optimize the performance of EEG based epilepsy seizures detection, in this work we are presenting a method based on two key algorithms. Here, we propose unique algorithm based on SWT (Stationary Wavelet Transform), for easier seizure analysis process, along with improved performance of the application of seizure detection algorithms. Then, we propose the algorithm for feature extraction that makes use of Higher Order Statistics of the coefficients that are calculated using Wavelet Packet Decomposition (WPD).This helps in improving the epilepsy seizures detection performance. The proposed methods helps to improve the overall efficiency and robustness of EEG based epilepsy seizures detection system.

scholarly journals Deep Learning for Epileptic Spike Detection

2018 ◽  
Vol 33 (2) ◽  
Author(s):  
Le Thanh Xuyen ◽  
Le Trung Thanh ◽  
Dinh Van Viet ◽  
Tran Quoc Long ◽  
Nguyen Linh Trung ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Spike Detection ◽  
Detection Model ◽  
Eeg Data ◽  
Discriminative Model ◽  
Epileptic Spikes ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike

In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes  detection system is highly useful and  meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in  data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike detection system is higher than $90$\%.  In particular, to construct  accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed. These features were then fed to the DBN which is modified from a generative model into a discriminative model to aim at classification accuracy. The experiment results indicate that it is possible to use deep learning models for epileptic spike detection with very high performance in item of sensitivity, selectivity, specificity and accuracy  92.82%,  97.83% , 96.41%, and 96.87%, respectively.

High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation

2013 ◽  
Vol 44 (4) ◽  
pp. 247-256 ◽  
Author(s):  
Chia-Ping Shen ◽  
Chih-Chuan Chen ◽  
Sheau -Ling Hsieh ◽  
Wei-Hsin Chen ◽  
Jia-Ming Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Detection System ◽  
Approximate Entropy ◽  
Seizure Detection ◽  
Clinical Validation

High performance Nanogold™-in situ hybridzation and its use in the detection of hybridized and PCR-amplified microscopical preparations

1996 ◽  
Vol 54 ◽  
pp. 896-897
Author(s):  
G. W. Hacker ◽  
I. Zehbe ◽  
J. Hainfeld ◽  
A.-H. Graf ◽  
C. Hauser-Kronberger ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Messenger Rna ◽  
High Performance ◽  
Detection System ◽  
Direct Methods ◽  
Genetic Alterations ◽  
Chain Reaction ◽  
Protein Encoding ◽  
Polymerase Chain

In situ hybridization (ISH) with biotin-labeled probes is increasingly used in histology, histopathology and molecular biology, to detect genetic nucleic acid sequences of interest, such as viruses, genetic alterations and peptide-/protein-encoding messenger RNA (mRNA). In situ polymerase chain reaction (PCR) (PCR in situ hybridization = PISH) and the new in situ self-sustained sequence replication-based amplification (3SR) method even allow the detection of single copies of DNA or RNA in cytological and histological material. However, there is a number of considerable problems with the in situ PCR methods available today: False positives due to mis-priming of DNA breakdown products contained in several types of cells causing non-specific incorporation of label in direct methods, and re-diffusion artefacts of amplicons into previously negative cells have been observed. To avoid these problems, super-sensitive ISH procedures can be used, and it is well known that the sensitivity and outcome of these methods partially depend on the detection system used.

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