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Development of Handheld Cardiac Event Monitoring System

机译:手持式心脏事件监测系统的开发

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This paper contributes the development, prototyping and analysis of proposed methodology on ARM (Advanced RISC Machine) in laboratory for automatic detection of arrhythmia beat in real-time for diagnosis of cardiovascular diseases. The methodology involves the integration of R peak detection algorithm, Principal Component Analysis for feature extraction and feedforward neural network architecture to classify generic heartbeats into six classes. The proposed methodology is implemented on ARM-based SoC (System-on-Chip) platform for diagnosis of six heartbeats. This developed system is validated by generating realtime ECG beats using MIT-BIH database and the output of the proposed system is monitored in the displaying device. The performance metrics of the developed system yields an overall accuracy of 92.81% with average sensitivity, specificity and positive predictivity of 92.68%, 98.51% and 92.42% respectively. Moreover, the developed system can be fabricated into a handheld device for automatic ECG beat monitoring.
机译:本文为实验室中用于实时自动检测心律失常的心律失常以诊断心血管疾病的ARM(高级RISC机器)方法的开发,原型设计和分析做出了贡献。该方法涉及R峰值检测算法,用于特征提取的主成分分析和前馈神经网络体系结构的集成,以将通用心跳分为六类。所提出的方法在基于ARM的SoC(片上系统)平台上实现,可用于诊断六个心跳。通过使用MIT-BIH数据库生成实时ECG搏动来验证此开发的系统,并在显示设备中监视所提议系统的输出。所开发系统的性能指标可产生92.81%的总体准确度,平均灵敏度,特异性和阳性预测性分别为92.68%,98.51%和92.42%。此外,可以将开发的系统装配到用于自动ECG搏动监测的手持设备中。

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