Mahsa Shoaran, Masoud Shahshahani, Masoud Farivar, J. Almajano, Amirhossein Shahshahani, A. Schmid, A. Bragin, Y. Leblebici, A. Emami-Neyestanak
{"title":"一种16通道1.1mm2可植入癫痫控制SoC,功耗为亚μ w /通道,采用0.18µm CMOS进行闭环刺激","authors":"Mahsa Shoaran, Masoud Shahshahani, Masoud Farivar, J. Almajano, Amirhossein Shahshahani, A. Schmid, A. Bragin, Y. Leblebici, A. Emami-Neyestanak","doi":"10.1109/VLSIC.2016.7573557","DOIUrl":null,"url":null,"abstract":"We present a 16-channel seizure detection system-on-chip (SoC) with 0.92μW/channel power dissipation in a total area of 1.1mm2 including a closed-loop neural stimulator. A set of four features are extracted from the spatially filtered neural data to achieve a high detection accuracy at minimal hardware cost. The performance is demonstrated by early detection and termination of kainic acid-induced seizures in freely moving rats and by offline evaluation on human intracranial EEG (iEEG) data. Our design improves upon previous works by over 40× reduction in power-area product per channel. This improvement is a key step towards integration of larger arrays with higher spatiotemporal resolution to further boost the detection accuracy.","PeriodicalId":6512,"journal":{"name":"2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits)","volume":"12 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"A 16-channel 1.1mm2 implantable seizure control SoC with sub-μW/channel consumption and closed-loop stimulation in 0.18µm CMOS\",\"authors\":\"Mahsa Shoaran, Masoud Shahshahani, Masoud Farivar, J. Almajano, Amirhossein Shahshahani, A. Schmid, A. Bragin, Y. Leblebici, A. Emami-Neyestanak\",\"doi\":\"10.1109/VLSIC.2016.7573557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a 16-channel seizure detection system-on-chip (SoC) with 0.92μW/channel power dissipation in a total area of 1.1mm2 including a closed-loop neural stimulator. A set of four features are extracted from the spatially filtered neural data to achieve a high detection accuracy at minimal hardware cost. The performance is demonstrated by early detection and termination of kainic acid-induced seizures in freely moving rats and by offline evaluation on human intracranial EEG (iEEG) data. Our design improves upon previous works by over 40× reduction in power-area product per channel. This improvement is a key step towards integration of larger arrays with higher spatiotemporal resolution to further boost the detection accuracy.\",\"PeriodicalId\":6512,\"journal\":{\"name\":\"2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits)\",\"volume\":\"12 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSIC.2016.7573557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIC.2016.7573557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A 16-channel 1.1mm2 implantable seizure control SoC with sub-μW/channel consumption and closed-loop stimulation in 0.18µm CMOS
We present a 16-channel seizure detection system-on-chip (SoC) with 0.92μW/channel power dissipation in a total area of 1.1mm2 including a closed-loop neural stimulator. A set of four features are extracted from the spatially filtered neural data to achieve a high detection accuracy at minimal hardware cost. The performance is demonstrated by early detection and termination of kainic acid-induced seizures in freely moving rats and by offline evaluation on human intracranial EEG (iEEG) data. Our design improves upon previous works by over 40× reduction in power-area product per channel. This improvement is a key step towards integration of larger arrays with higher spatiotemporal resolution to further boost the detection accuracy.