ARM-based arrhythmia beat monitoring system
This paper aims for accurate diagnosis of arrhythmia beats in real time to enhance the health care service for cardiovascular diseases. The proposed methodology for the diagnosis involves the integration of the R-peak detection algorithm, FFT (fast fourier transform) based discrete wavelet transform for feature extraction and feedforward based Neural Network Architecture to classify generic cardiac beat classes into eight categories namely Right Bundled Block, Left Bundled Block, Preventricular Contraction (PVC), Atrial Premature Contraction (APC), Ventricular Flutter wave (VF), Paced Beat, Ventricular Escape (VE) and Normal beat. The paper contributes the development, prototyping and analysis of proposed methodology on ARM (Advanced RISC Machine) based SoC (System-on-Chip) in laboratory setup. This system is validated by generating real-time ECG signals using MIT-BIH database while the output of the system is monitored on the displaying device. The performance analysis of the proposed methodology implemented on the microcontroller based system is computed by performing the experiment which achieves a high overall accuracy of 97.4% with average sensitivity (Se S e ) of 97.57%, specificity (Sp S p ) of 99.59% and positive predictivity (Pp P p ) of 97.93%. The system provides an assistive diagnostic solution to the users to lead a healthy lifestyle. Moreover, the ARM-based system can be fabricated into a handheld device for reliable automatic monitoring of the condition of heart by patients.
arrhythmia beat discrete wavelet transform neural network electrocardiography arm microcontroller