Ingo Hoyer, Alexander Utz, André Lüdecke, Karsten Seidl, Özgü Roßman, Lukas Straczek, Onur Akboyraz, Sebastian Hessel
{"title":"ARTEMIS项目:基于边缘人工智能的心电信号分类混合信号IC","authors":"Ingo Hoyer, Alexander Utz, André Lüdecke, Karsten Seidl, Özgü Roßman, Lukas Straczek, Onur Akboyraz, Sebastian Hessel","doi":"10.1515/cdbme-2023-1082","DOIUrl":null,"url":null,"abstract":"Abstract Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals\",\"authors\":\"Ingo Hoyer, Alexander Utz, André Lüdecke, Karsten Seidl, Özgü Roßman, Lukas Straczek, Onur Akboyraz, Sebastian Hessel\",\"doi\":\"10.1515/cdbme-2023-1082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.\",\"PeriodicalId\":10739,\"journal\":{\"name\":\"Current Directions in Biomedical Engineering\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Directions in Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/cdbme-2023-1082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Directions in Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/cdbme-2023-1082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals
Abstract Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.