An ECG Processor for the Detection of Eight Cardiac Arrhythmias with Minimum False Alarms

Muhammad Sohail, Zain Taufique, S. Abubakar, Wala Saadeh, Muhammad Awais Bin Altaf
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引用次数: 20

Abstract

An Electrocardiography (ECG) based processor for eight Cardiac arrhythmias (CA) detection with smart priority logic is presented to minimize the false alarms. The processor utilizes a Multi-Level Linear Support Vector Machine (ML-LSVM) classifiers with one-vs-all approach to distinguish the different CAs. The classification is solely based on 5 features including R-wave, S-wave, T-wave, R-R interval and Q-S interval. The processor employs a priority logic to prioritize the detected conditions if more than one condition are detected. The system is implemented using CMOS 180nm with an area of 0.18mm2 and validated using 83 patient’s recordings from Physionet Arrhythmia Database and Creighton University Database. The proposed processor consumes 0.91uW with an average classification accuracy of 98.5% while reducing the false alarms by 99%, which is 30% superior performance compared to conventional systems.
一种检测八种心律失常且虚警最小的心电处理器
提出了一种基于心电图的八种心律失常检测处理器,该处理器具有智能优先级逻辑,可最大限度地减少误报。该处理器利用多层级线性支持向量机(ML-LSVM)分类器,采用一对一的方法来区分不同的ca。仅根据r波、s波、t波、R-R区间和Q-S区间5个特征进行分类。如果检测到多个条件,处理器采用优先级逻辑对检测到的条件进行优先级排序。该系统采用面积为0.18mm2的CMOS 180nm实现,并使用来自Physionet心律失常数据库和Creighton大学数据库的83例患者记录进行验证。该处理器功耗为0.91uW,平均分类准确率为98.5%,同时减少了99%的误报,与传统系统相比,性能提高了30%。
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