{"title":"ADDHard","authors":"Sai Manoj Pudukotai Dinakarrao, A. Jantsch","doi":"10.1145/3194554.3194647","DOIUrl":null,"url":null,"abstract":"Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detection. ADDHard has the advantages of low-power consumption and a small foot print. ADDHard is suitable especially for resource constrained systems such as body wearable devices. Its implementation was tested with the MIT-BIH arrhythmia database and achieved an accuracy of 97.28% with a specificity of 98.25% on average.","PeriodicalId":215940,"journal":{"name":"Proceedings of the 2018 on Great Lakes Symposium on VLSI","volume":"258 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"ADDHard\",\"authors\":\"Sai Manoj Pudukotai Dinakarrao, A. Jantsch\",\"doi\":\"10.1145/3194554.3194647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detection. ADDHard has the advantages of low-power consumption and a small foot print. ADDHard is suitable especially for resource constrained systems such as body wearable devices. Its implementation was tested with the MIT-BIH arrhythmia database and achieved an accuracy of 97.28% with a specificity of 98.25% on average.\",\"PeriodicalId\":215940,\"journal\":{\"name\":\"Proceedings of the 2018 on Great Lakes Symposium on VLSI\",\"volume\":\"258 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 on Great Lakes Symposium on VLSI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3194554.3194647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 on Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3194554.3194647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detection. ADDHard has the advantages of low-power consumption and a small foot print. ADDHard is suitable especially for resource constrained systems such as body wearable devices. Its implementation was tested with the MIT-BIH arrhythmia database and achieved an accuracy of 97.28% with a specificity of 98.25% on average.
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