Design and Implementation of A Novel Real Time P-QRS-T Waves Detection Algorithm

Electrocardiogram (ECG) contains a large amount of information on physiological and pathological of human body. In a complete cardiac cycle, P, QRS complex and T waves are included, which correspond to the electrical activity of the heart respectively. As an important part of ECG signal processing, characteristic points detection algorithm is of great significance in the calculation and analysis of cardiac-specific parameters in heart disease analysis and heart monitoring system. In order to realize real-time detection of P, QRS complex and T waves, this paper proposes a novel algorithm, which is based on choosing the optimal bandwidth-band pass filter and mainly uses the peak threshold method to detect these waves. This filter achieves QRS complex enhancement and noise reduction simultaneously. After capturing the QRS complex, we further generate a triangular signal to cross-correlate with the main signal, P and T waves were also successfully detected. This algorithm is tested on the MIT-BIH Arrhythmia database, the QT database and evaluated using MATLAB R2016b software.

[1]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[2]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Mohammad R. Homaeinezhad,et al.  A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles , 2014, Comput. Biol. Medicine.

[4]  Ibrahim Elshafiey,et al.  A Power Line Interference Canceler using Wavelet Transform and Adaptive Filter for ECG Signal , 2017, 2017 International Conference on Computer and Applications (ICCA).

[5]  Chieh-Li Chen,et al.  A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices , 2017, Sensors.

[6]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[7]  Unsang Park,et al.  R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope , 2017, Journal of healthcare engineering.

[8]  Bernadette Dorizzi,et al.  ECG signal analysis through hidden Markov models , 2006, IEEE Transactions on Biomedical Engineering.

[9]  Kamrul Hasan,et al.  Automatic detection of ECG wave boundaries using empirical mode decomposition , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[11]  Marko Sarlija,et al.  A convolutional neural network based approach to QRS detection , 2017, Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis.

[12]  Olivier Meste,et al.  Quantifying the PR Interval Pattern During Dynamic Exercise and Recovery , 2009, IEEE Transactions on Biomedical Engineering.