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  Embedded Hardware for Online Monitoring of ECG Signal  
  Authors : Bhagyashree K Patil; Seema H Rajput; Durgaprasad K Kamat; Dr. Vijay M. Wadhai
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The proposed work presents the principle of low cost embedded system which gives essential ECG monitoring system. For ECG signal in real time, embedded C is used for programming the proposed system hardware. Embedded system consists of 32bit ARM LPC2138, MAX232, Amplifier circuit block, ECG electrodes. System gives essential real time ECG signal values. ECG signal related parameters are analyzed like wave’s amplitude, heart beats, blood pressure and RR interval. It also gives normal and abnormal status of ECG signal. Different algorithms like wavelet transform, extended kalman filter, extended kalman smoother and linear discriminant analysis classifier are used for filtering and analysis of the ECG signal and its parameters respectively. Wavelet Transform gives better Accuracy of 97.20% and Detection error ratio of 0.064, Positive Prediction of 93.91% as compared with EKF and EKS. System have advantages like low cost, better results in real time, versatility.


Published In : IJCSN Journal Volume 4, Issue 5

Date of Publication : October 2015

Pages : 771 - 779

Figures :08

Tables : 03

Publication Link : Embedded Hardware for Online Monitoring of ECG Signal




Bhagyashree K Patil : Dept of E & TC, Sinhgad Academy of Engg, Pune, India

Seema H Rajput : Asst Prof, Dept of E & TC, Sinhgad Academy of Engg, Pune, India

Durgaprasad K Kamat : Asst Prof, Dept of E & TC, Sinhgad Academy of Engg, Pune And Research Scholar, SCOE, Pune, India

Dr. Vijay M. Wadhai : Principal, Sinhgad Academy of Engg, Pune, India








Embedded ECG Monitoring System

Detects Normal and Abnormal Status

Wavelet Transform

Extended Kalman Filter

Extended Kalman Smoother and LDA

The proposed embedded system design is used to detect the normal and abnormal problems generated in ECG signal measured in real time. System obtained is exact ECG signal in real time on Visual basic and value comes from the signal that is stored in drive on Personal Computer. After that, ECG signal parameters are correctly analyzed on MATLAB with the help of wavelet transform with LDA classifier which gives better performance than EKF and EKS algorithm. Wavelet Transform gives better Accuracy of 97.20% and Detection error ratio is 0.064, Positive Prediction 93.91% as compared with EKF and EKS. From the results, we have concluded that the real time system gives good results as MIT-BIH data. Proposed System is used for the home and hospitals for heart patients. It is cost effective.










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