Cardiac arrhythmias is a very uncommon life threating arrhythmia which can even cause sudden death. Healthcare professionals are always looking to find out the ways in order to reduce the death rate. The new method of feature extraction and classification of arrhythmias has been developed by the authors of this paper in their previous works. In this paper, authors have proposed the methodology for the development of a real-time system for efficient processing of arrhythmic signals in order to differentiate between normal and abnormal patients. The purpose of this work is to develop a real-time system for processing the real-time signals or signals obtained from MIT-BIH arrhythmia database. For carrying out this work, we have taken the signals from MIT-BIH Supraventricular arrhythmiaÂ Â  database and MIT-BIH Fantasia database. Authors have achieved 100% accuracy by using this method. Keywords: MIT-BIH, Cardiac arrhythmias, real-time system

Saraswat, Shipra

Shukla, Sachidanand

Srivastava, Geetika

Journal of Drug Delivery and Therapeutics (JDDT)

 Saraswat et al                                                                                                          Journal of Drug Delivery & Therapeutics. 2017; 7(6):7-10                        ISSN: 2250-1177                                                                              [7]                                                                            CODEN (USA): JDDTAO Available online on 15.11.2017 at http://jddtonline.info Journal of Drug Delivery and Therapeutics Open Access to Pharmaceutical and Medical Research © 2011-17, publisher and licensee JDDT, This is an Open Access article which permits unrestricted non-commercial use, provided the original work is properly cited Open  Access                                                                                                                     Research Article REAL TIME SYSTEM FOR EFFICIENT PROCESSING OF CARDIAC ARRHYTHMIAS SIGNALS *Shipra Saraswat1, Geetika Srivastava2, Sachidanand Shukla3 1Department of Computer Science, Amity University Uttar Pradesh, India 2Department of Electronics and Communication, Amity University Uttar Pradesh, India 3Department of Physics and Electronics, Dr. RML Avadh University, Faizabad, India  ABSTRACT Cardiac arrhythmias is a very uncommon life threating arrhythmia which can even cause sudden death. Healthcare professionals are always looking to find out the ways in order to reduce the death rate. The new method of feature extraction and classification of arrhythmias has been developed by the authors of this paper in their previous works. In this paper, authors have proposed the methodology for the development of a real-time system for efficient processing of arrhythmic signals in order to differentiate between normal and abnormal patients. The purpose of this work is to develop a real-time system for processing the real-time signals or signals obtained from MIT-BIH arrhythmia database. For carrying out this work, we have taken the signals from MIT-BIH Supraventricular arrhythmia   database and MIT-BIH Fantasia database. Authors have achieved 100% accuracy by using this method. Keywords: MIT-BIH, Cardiac arrhythmias, real-time system.   Article Info: Received 27 Aug, 2017; Review Completed 17 Oct, 2017; Accepted 22 Oct, 2017; Available online 15 Nov, 2017 Cite this article as: Saraswat S, Srivastava G, Shukla S, Real time system for efficient processing of cardiac arrhythmias signals, Journal of Drug Delivery and Therapeutics. 2017; 7(6):7-10 DOI: http://dx.doi.org/10.22270/jddt.v7i6.1518  *Address for Correspondence  Shipra Saraswat, Department of Computer Science, Amity University Uttar Pradesh, India. Email: Sshipra1510@gmail.com   INTRODUCTION Real time system is a computing system (software or sometimes hardware) associated with time limits and generates output subject to a real time constraint. These systems must ensure to produce response within specified time boundaries or often described to ascertain deadlines. For example we can say that from event to response given by system1. The accuracy of these systems totally depends on their sequential aspects as well as their operational aspects. Real time systems produce responses within milliseconds and may be in microseconds also. These systems does not take guarantee to generate a response within any fixed boundaries but it must ensure to give expected response which they may be given2. The description of real time systems as like which regulates an environment by taking data, processing them and generating the results quickly within specified time. This concept is widely used in simulation which means clock of simulation’s process at real clock means without any delay the output is obtained. So, in this work we proposed such a real time system which takes input as arrhythmic signal, processes that signal and produces output within fixed boundaries in terms of normal and abnormal3. Related Work Authors have studied the natural fluctuations of heart rate by using block agents and posture positions. The spectral analysis of heart rate is a powerful tool for automatic nervous system activity4. Hidden Markov model is used for analyzing cardiac arrhythmias by the researchers of paper5. In this work, they used the combination of structural and statistical knowledge of experimental signals in one single valued model. This approach can enhance the analysis of supraventricular arrhythmias with the help of exact analysis of signals and P wave information. In paper6, authors use the holter monitors and real time system capabilities in order to provide an accurate recognition of ECG signals using smartphones. They developed two wearable smartphones for recognizing heart disorders. Authors of  Saraswat et al                                                                                                          Journal of Drug Delivery & Therapeutics. 2017; 7(6):7-10                        ISSN: 2250-1177                                                                              [8]                                                                            CODEN (USA): JDDTAO paper 7, developed a highly accurate tool for clinical practices. The method they adopted is using a catheter navigating system for determining the local electrograms. Through this they recognize the cardiac chambers which will be helpful in analyzing the different patterns of arrhythmic signals. METHODOLOGY For analyzing any heart disorder the QRS information is very necessary step 8. So, authors analyses the QRS portions of signals and processes in order to obtain an accurate results. The methodology we adopted for carrying out this works are as follows. Firstly we take ECG arrhythmic signals already stored in our computer system in text file or xls file. That arrhythmic signals are fed into our personal computer system with Matlab created environment. Then the data processing and feature extraction process has been carried out. For data processing and extraction of features, authors have adopted the discrete wavelet transformation method 9. By using this method we extract the ECG signals on five levels in terms of approximation and detailed coefficients like approx. level 1 to approx. level 5 and detail level 1 to detail level 5 10. On the basis of that extracted coefficients from ECG signals, we have calculated the mean results of extracted features by using Matlab programming. Authors find a lot of difference in mean values of normal and abnormal signals. Our GUI has trained on recognizing these mean values. Then, GUI produces output in terms of normal and abnormal. On the second part, authors have placed the ECG electrodes on human body that directly fed ECG signals in Arduino board with ECG shield. With the help of USB port, that real time signals directly processes with the help of GUI and presents the output as shown in results section of this paper. Below displayed Fig. 1 shows architecture of proposed system.   Figure 1: Architecture of proposed system  RESULTS AND DISCUSSION The results of this work are generated by using Matlab programming by implementing    the    above    explained methodology. Below Fig. 2 and Fig. 3 illustrates the experimental ECG signals (abnormal   and   normal)   respectively have been chosen for realizing the efficiency of this proposed method.  Figure 2:   Experimental arrhythmic signal  Figure 3: Experimental normal ECG signal These experimental signals when fed into the computer system for processing and feature extraction; produce output shown in Fig. 4 and Fig. 5. The GUI has been developed by the authors of this paper by using the concept of Matlab programming.  Saraswat et al                                                                                                          Journal of Drug Delivery & Therapeutics. 2017; 7(6):7-10                        ISSN: 2250-1177                                                                              [9]                                                                            CODEN (USA): JDDTAO  Figure 4: Data processing of arrhythmic signal on proposed system  Figure 5: Data processing of normal signal on proposed system  CONCLUSION This paper develops a real time system for efficient processing of arrhythmic signals for separating between normal and abnormal signals. Authors firstly decompose the experimental signals and obtained approximate and detailed coefficients as approx. level 1 to approx. level 5 and detailed coefficients from detail level 1 to detail level 5. After processing of this data our system produces output in terms of normal and abnormal. The achieved accuracy of this proposed system is found to be 100%. This proposed developed system is found to be very beneficial for health care professionals in identifying and treating arrhythmic patients. Conflicts of Interest: All authors declare that they have no conflicts of interest.   Saraswat et al                                                                                                          Journal of Drug Delivery & Therapeutics. 2017; 7(6):7-10                        ISSN: 2250-1177                                                                              [10]                                                                            CODEN (USA): JDDTAO REFERENCES 1. Abenstein, J.P. and Tompkins, W.J., “A new data-reduction algorithm for real-time ECG analysis” EEE Transactions on Biomedical Engineering, 1982; 1:43-48. 2. Bahoura M, Hassani M and Hubin M., “DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis” Computer Methods and Programs in Biomedicine, 1997; 52(1):35-44. 3. Rodriguez J, Goni A and Illarramendi A, “Real-time classification of ECGs on a PDA” IEEE Transactions on Information Technology in Biomedicine, 2 005 ;  9 (1 ) : 23-34. 4. Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon DA, Kilborn KM, Barger AC, Shannon DC and Cohen RJ, “Assessment of autonomic function in humans by heart rate spectral analysis” American Journal of Physiology-Heart and Circulatory Physiology, 1985; 248(1):151-153. 5. Coast DA, Stern RM, Cano GG and Briller SA, “An approach to cardiac arrhythmia analysis using hidden Markov models” IEEE  Transactions  on biomedical Engineering, 1990; 37(9):826-836. 6. Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H and Cheng AC, “A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram  processing”  IEEE Transactions on Information Technology in Biomedicine, 2 0 1 0 ;  14(3):734-740. 7. Gepstein L, Hayam G and Ben-Haim SA, “A novel method for nonfluoroscopic catheter-based electroanatomical mapping of the heart”, 1997; 95(6):1611-1622. 8. Saraswat S, Srivastava G and Shukla S, “Review: Comparison of QRS detection algorithms” International Conference on Computing, Communication & Automation (ICCCA), 2 0 1 5 ;  354-359. 9. Alfaouri, M. and Daqrouq, K., “ECG signal denoising by wavelet transform thresholding” American Journal of applied sciences, 2008; 5(3):276-281. 10. Saraswat S, Srivastava G and Shukla SN,” Malignant Ventricular Ectopy Classification using Wavelet Transformation and Probabilistic Neural Network Classifier” Indian Journal of Science and Technology, 2016;  9(40):1-5. 

REAL TIME SYSTEM FOR EFFICIENT PROCESSING OF CARDIAC ARRHYTHMIAS SIGNALS

http://jddtonline.info/index.php/jddt/article/download/1518/872

REAL TIME SYSTEM FOR EFFICIENT PROCESSING OF CARDIAC ARRHYTHMIAS SIGNALS

Abstract

Similar works

Full text

Available Versions

Journal of Drug Delivery and Therapeutics (JDDT)