FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm
This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learnin...
Gespeichert in:
Veröffentlicht in: | Discover Applied Sciences 2024-07, Vol.6 (8), p.383, Article 383 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 8 |
container_start_page | 383 |
container_title | Discover Applied Sciences |
container_volume | 6 |
creator | Sajja, Amrita Rooban, S. |
description | This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).
Article Highlights
First one is data compression which was successfully achieved with Cellular Automata techniques and 85% memory is saved.
Second the compressed data set trained to the machine learning algorithm was developed and implemented in the FPGA with very negligible delay and very high accuracy.
The seizure affected people will have very less time to take the medical diagnosis. So the proposed work will be helpful to detect the abnormal states so they can react early for damage prevention. |
doi_str_mv | 10.1007/s42452-024-06060-4 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3082048335</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3082048335</sourcerecordid><originalsourceid>FETCH-LOGICAL-c244t-dc999cdf20408078d01481f8510093a76c448289e714d92c4cfe16924a2866443</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIVKU_wMkS54Afm8Q-VlVbkCrBAc6W6zipUeIUOznA1-M2SHBCe9jXzGh3ELql5J4SUj5EYJCzjDDISJEigws044RAJllBL__U12gRo9uTHIDxgssZ2m9etkvsumNrO-sHPbje477Gehz6LnUGR-u-xmBxZQdrzmvn8Xq9xdE1XrcRj9H5BnfaHJy3uLU6-NNAt00f3HDobtBVnXB28ZPn6G2zfl09Zrvn7dNqucsMAxiyykgpTVUzAkSQUlSEgqC1yNOPkuuyMACCCWlLCpVkBkxtaSEZaCaKAoDP0d2kewz9x2jjoN77MZxOVJyIJCs4zxOKTSgT-hiDrdUxuE6HT0WJOtmpJjtVslOd7VQnaT6RYgL7xoZf6X9Y3-DLdtY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3082048335</pqid></control><display><type>article</type><title>FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm</title><source>DOAJ Directory of Open Access Journals</source><source>SpringerNature Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><source>Springer Nature OA/Free Journals</source><creator>Sajja, Amrita ; Rooban, S.</creator><creatorcontrib>Sajja, Amrita ; Rooban, S.</creatorcontrib><description>This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).
Article Highlights
First one is data compression which was successfully achieved with Cellular Automata techniques and 85% memory is saved.
Second the compressed data set trained to the machine learning algorithm was developed and implemented in the FPGA with very negligible delay and very high accuracy.
The seizure affected people will have very less time to take the medical diagnosis. So the proposed work will be helpful to detect the abnormal states so they can react early for damage prevention.</description><identifier>ISSN: 3004-9261</identifier><identifier>ISSN: 2523-3963</identifier><identifier>EISSN: 3004-9261</identifier><identifier>EISSN: 2523-3971</identifier><identifier>DOI: 10.1007/s42452-024-06060-4</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Applied and Technical Physics ; Artificial intelligence ; Cellular automata ; Chemistry/Food Science ; Clock synchronization ; Convulsions & seizures ; Damage detection ; Damage prevention ; Data compression ; Design ; Earth Sciences ; EEG ; Electrocardiography ; Electroencephalography ; Engineering ; Environment ; Epilepsy ; Field programmable gate arrays ; Harnesses ; Learning algorithms ; Lookup tables ; Machine learning ; Market entry ; Materials Science ; Medical diagnosis ; Monitoring ; Neural networks ; Physiology ; Predictions ; Real time ; Seizures ; Signal processing ; Software ; Synchronization ; Telemedicine ; Time synchronization</subject><ispartof>Discover Applied Sciences, 2024-07, Vol.6 (8), p.383, Article 383</ispartof><rights>The Author(s) 2024</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c244t-dc999cdf20408078d01481f8510093a76c448289e714d92c4cfe16924a2866443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,865,27929,27930</link.rule.ids></links><search><creatorcontrib>Sajja, Amrita</creatorcontrib><creatorcontrib>Rooban, S.</creatorcontrib><title>FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm</title><title>Discover Applied Sciences</title><addtitle>Discov Appl Sci</addtitle><description>This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).
Article Highlights
First one is data compression which was successfully achieved with Cellular Automata techniques and 85% memory is saved.
Second the compressed data set trained to the machine learning algorithm was developed and implemented in the FPGA with very negligible delay and very high accuracy.
The seizure affected people will have very less time to take the medical diagnosis. So the proposed work will be helpful to detect the abnormal states so they can react early for damage prevention.</description><subject>Algorithms</subject><subject>Applied and Technical Physics</subject><subject>Artificial intelligence</subject><subject>Cellular automata</subject><subject>Chemistry/Food Science</subject><subject>Clock synchronization</subject><subject>Convulsions & seizures</subject><subject>Damage detection</subject><subject>Damage prevention</subject><subject>Data compression</subject><subject>Design</subject><subject>Earth Sciences</subject><subject>EEG</subject><subject>Electrocardiography</subject><subject>Electroencephalography</subject><subject>Engineering</subject><subject>Environment</subject><subject>Epilepsy</subject><subject>Field programmable gate arrays</subject><subject>Harnesses</subject><subject>Learning algorithms</subject><subject>Lookup tables</subject><subject>Machine learning</subject><subject>Market entry</subject><subject>Materials Science</subject><subject>Medical diagnosis</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Physiology</subject><subject>Predictions</subject><subject>Real time</subject><subject>Seizures</subject><subject>Signal processing</subject><subject>Software</subject><subject>Synchronization</subject><subject>Telemedicine</subject><subject>Time synchronization</subject><issn>3004-9261</issn><issn>2523-3963</issn><issn>3004-9261</issn><issn>2523-3971</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9UMtOwzAQtBBIVKU_wMkS54Afm8Q-VlVbkCrBAc6W6zipUeIUOznA1-M2SHBCe9jXzGh3ELql5J4SUj5EYJCzjDDISJEigws044RAJllBL__U12gRo9uTHIDxgssZ2m9etkvsumNrO-sHPbje477Gehz6LnUGR-u-xmBxZQdrzmvn8Xq9xdE1XrcRj9H5BnfaHJy3uLU6-NNAt00f3HDobtBVnXB28ZPn6G2zfl09Zrvn7dNqucsMAxiyykgpTVUzAkSQUlSEgqC1yNOPkuuyMACCCWlLCpVkBkxtaSEZaCaKAoDP0d2kewz9x2jjoN77MZxOVJyIJCs4zxOKTSgT-hiDrdUxuE6HT0WJOtmpJjtVslOd7VQnaT6RYgL7xoZf6X9Y3-DLdtY</recordid><startdate>20240717</startdate><enddate>20240717</enddate><creator>Sajja, Amrita</creator><creator>Rooban, S.</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope></search><sort><creationdate>20240717</creationdate><title>FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm</title><author>Sajja, Amrita ; Rooban, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c244t-dc999cdf20408078d01481f8510093a76c448289e714d92c4cfe16924a2866443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Applied and Technical Physics</topic><topic>Artificial intelligence</topic><topic>Cellular automata</topic><topic>Chemistry/Food Science</topic><topic>Clock synchronization</topic><topic>Convulsions & seizures</topic><topic>Damage detection</topic><topic>Damage prevention</topic><topic>Data compression</topic><topic>Design</topic><topic>Earth Sciences</topic><topic>EEG</topic><topic>Electrocardiography</topic><topic>Electroencephalography</topic><topic>Engineering</topic><topic>Environment</topic><topic>Epilepsy</topic><topic>Field programmable gate arrays</topic><topic>Harnesses</topic><topic>Learning algorithms</topic><topic>Lookup tables</topic><topic>Machine learning</topic><topic>Market entry</topic><topic>Materials Science</topic><topic>Medical diagnosis</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Physiology</topic><topic>Predictions</topic><topic>Real time</topic><topic>Seizures</topic><topic>Signal processing</topic><topic>Software</topic><topic>Synchronization</topic><topic>Telemedicine</topic><topic>Time synchronization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sajja, Amrita</creatorcontrib><creatorcontrib>Rooban, S.</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Discover Applied Sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sajja, Amrita</au><au>Rooban, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm</atitle><jtitle>Discover Applied Sciences</jtitle><stitle>Discov Appl Sci</stitle><date>2024-07-17</date><risdate>2024</risdate><volume>6</volume><issue>8</issue><spage>383</spage><pages>383-</pages><artnum>383</artnum><issn>3004-9261</issn><issn>2523-3963</issn><eissn>3004-9261</eissn><eissn>2523-3971</eissn><abstract>This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).
Article Highlights
First one is data compression which was successfully achieved with Cellular Automata techniques and 85% memory is saved.
Second the compressed data set trained to the machine learning algorithm was developed and implemented in the FPGA with very negligible delay and very high accuracy.
The seizure affected people will have very less time to take the medical diagnosis. So the proposed work will be helpful to detect the abnormal states so they can react early for damage prevention.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s42452-024-06060-4</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 3004-9261 |
ispartof | Discover Applied Sciences, 2024-07, Vol.6 (8), p.383, Article 383 |
issn | 3004-9261 2523-3963 3004-9261 2523-3971 |
language | eng |
recordid | cdi_proquest_journals_3082048335 |
source | DOAJ Directory of Open Access Journals; SpringerNature Journals; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection; Springer Nature OA/Free Journals |
subjects | Algorithms Applied and Technical Physics Artificial intelligence Cellular automata Chemistry/Food Science Clock synchronization Convulsions & seizures Damage detection Damage prevention Data compression Design Earth Sciences EEG Electrocardiography Electroencephalography Engineering Environment Epilepsy Field programmable gate arrays Harnesses Learning algorithms Lookup tables Machine learning Market entry Materials Science Medical diagnosis Monitoring Neural networks Physiology Predictions Real time Seizures Signal processing Software Synchronization Telemedicine Time synchronization |
title | FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T02%3A35%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=FPGA%20implementation%20of%20automatic%20seizure%20detection%20in%20EEG%20signals%20using%20machine%20learning%20algorithm&rft.jtitle=Discover%20Applied%20Sciences&rft.au=Sajja,%20Amrita&rft.date=2024-07-17&rft.volume=6&rft.issue=8&rft.spage=383&rft.pages=383-&rft.artnum=383&rft.issn=3004-9261&rft.eissn=3004-9261&rft_id=info:doi/10.1007/s42452-024-06060-4&rft_dat=%3Cproquest_cross%3E3082048335%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3082048335&rft_id=info:pmid/&rfr_iscdi=true |