ECG Monitoring: Present Status and Future Trend

Abstract ECG is one of the important physiological parameters and is required for monitoring and diagnosis of common cardiac patients. Nowadays, it is an integrated part of generalized monitoring system in ICU and other critical care units. With the development of sophisticated instrumentation systems and modern signal processing technologies, the monitoring and automation in analysis of acquired data makes clinical decision faster and more reliable. The present article discusses the conventional ECG monitoring methodology and recent technologies used for signal acquisition and processing for ECG analysis. As the signal strength (amplitude, frequency, etc.) is feeble in biosignals like ECG, proper data acquisition hardware should be designed for capturing and further processing for information extraction. With the development of computerized automatic signal processing technologies, it becomes easier to develop a biosignal processing and interpretation system. In this article, the application of modern signal processing tools for electrocardiogram (ECG) signal analysis for classification and detection of rhythmic abnormalities is also discussed. For completeness, basics of the heart and generation and interpretation of ECG are also included along with the present trends toward wireless ECG monitoring and few specialized ECG monitoring applications.

[1]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: Rangayyan/Biomedical , 2015 .

[2]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[3]  G. Clifford,et al.  Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors. , 2011, American journal of obstetrics and gynecology.

[4]  Giuseppe Iannaccone,et al.  Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring , 2016, IEEE Sensors Journal.

[5]  M Mitra,et al.  Study of atrial activities for abnormality detection by phase rectified signal averaging technique , 2015, Journal of medical engineering & technology.

[6]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[7]  Raúl Alcaraz,et al.  Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings , 2011, IEEE Transactions on Biomedical Engineering.

[8]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[9]  Steffen Leonhardt,et al.  Automatic Detection of Atrial Fibrillation in Cardiac Vibration Signals , 2013, IEEE Journal of Biomedical and Health Informatics.

[10]  Ki H. Chon,et al.  Time-Varying Coherence Function for Atrial Fibrillation Detection , 2013, IEEE Transactions on Biomedical Engineering.

[11]  Madhuchhanda Mitra,et al.  Characterizing Atrial Fibrillation in Empirical Mode Decomposition Domain , 2016 .

[12]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[13]  P.S. Hamilton,et al.  A comparison of adaptive and nonadaptive filters for reduction of power line interference in the ECG , 1996, IEEE Transactions on Biomedical Engineering.

[14]  Mohd. Alauddin Mohd. Ali,et al.  Real-time signal processing for fetal heart rate monitoring , 2003, IEEE Transactions on Biomedical Engineering.

[15]  Wei Xiang,et al.  An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare , 2016, Journal of Medical Systems.

[16]  K. Egiazarian,et al.  Suppression of electromyogram interference on the electrocardiogram by transform domain denoising , 2001, Medical and Biological Engineering and Computing.

[17]  S. Pei,et al.  Elimination of AC interference in electrocardiogram using IIR notch filter with transient suppression. , 1995, IEEE transactions on bio-medical engineering.

[18]  Truong Q. Nguyen,et al.  Comparing stress ECG enhancement algorithms , 1996 .

[19]  Jacek M. Leski Robust weighted averaging [of biomedical signals] , 2002, IEEE Transactions on Biomedical Engineering.

[20]  Jacek M. Leski,et al.  ECG baseline wander and powerline interference reduction using nonlinear filter bank , 2005, Signal Process..

[21]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.

[22]  Ki H. Chon,et al.  Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches , 2012, IEEE Transactions on Biomedical Engineering.

[23]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[24]  Alan Murray,et al.  Estimation of the ECG signal spectrum during ventricular fibrillation using the fast Fourier transform and maximum entropy methods , 1993, Proceedings of Computers in Cardiology Conference.

[25]  Refet Firat Yazicioglu,et al.  A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[26]  Z. Alfirevic,et al.  Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. , 2017, The Cochrane database of systematic reviews.

[27]  Charles K. Chui,et al.  An Introduction to Wavelets , 1992 .

[28]  James McNames,et al.  Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes , 2004, IEEE Transactions on Biomedical Engineering.

[29]  E. Nemati,et al.  A wireless wearable ECG sensor for long-term applications , 2012, IEEE Communications Magazine.

[30]  Madhuchhanda Mitra,et al.  Imposed target based modification of Taguchi method for feature optimisation with application in arrhythmia beat detection , 2016, Expert Syst. Appl..

[31]  J S Paul,et al.  Data processing of stress ECGs using discrete cosine transform. , 1998, Computers in biology and medicine.

[32]  M. Mitra,et al.  Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method , 2010 .

[33]  L. Sörnmo,et al.  Time-varying digital filtering of ECG baseline wander , 1993, Medical and Biological Engineering and Computing.

[34]  C D McManus,et al.  Characterization and elimination of AC noise in electrocardiograms: a comparison of digital filtering methods. , 1993, Computers and biomedical research, an international journal.

[35]  Ergun Erçelebi,et al.  Electrocardiogram signals de-noising using lifting-based discrete wavelet transform , 2004, Comput. Biol. Medicine.

[36]  Chao Huang,et al.  A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm , 2011, IEEE Transactions on Biomedical Engineering.

[37]  Allan Kardec Barros,et al.  independent , 2006, Gumbo Ya Ya.

[38]  Vicente Zarzoso,et al.  Spatial Variability of the 12-Lead Surface ECG as a Tool for Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation , 2013, IEEE Transactions on Biomedical Engineering.