Design and Analysis of Electrocardiograph (ECG) Signal for long term continuous heart rate monitoring system

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1 Volume 119 No , ISSN: (on-line version) url: Design and Analysis of Electrocardiograph (ECG) Signal for long term continuous heart rate monitoring system Dr.M.Anto Bennet 1, Bhavani.B 2, Hema Priya.C.A 3 1 Professor of Electronics and Communication Engineering, Vel Tech,Chennai,India 2,3 UG Scholar,Department of Electronics and Communication Engineering,Vel Tech,Chennai,India * Corresponding author s priya4anandan@gmail.com 3 ABSTRACT Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper describes the design of real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. However these techniques are severely hampered by motion artifacts they are limited to heart rate detection. To address these shortcomings we present a new ECG wearable that is similar to the clinical approach for heart monitoring. Our device is weightless and is ultra-low power, extending the battery lifetime to over a month to make the device more appropriate for in-home health care applications. This device uses two electrodes activated by the user to measure the voltage across the wrists. The electrodes are made of a flexible ink and can be painted on to the device casing, making it adaptable for different shapes and users. Also show the result of heart rate of beats per minute (bpm) based on the R-R interval (peaks) calculation. That means whether the heart function is normal or abnormal (Tachycardia, Bradycardia). Keywords: Electrocardiogram (ECG), standard deviation of all NN intervals (SDNN), chronic heart failure (CHF) INTRODUCTION Electrocardiogram (ECG) represents electrical activity of human heart. ECG is composed from 5 waves - P, Q, R, S and T. This signal could be measured by electrodes in human body with typical engagement. Signals from these electrodes are brought into simple electrical circuits with amplifiers and analogue digital converters. The main problem of digitalized signal is interference with other noisy signals like power supply network 50 Hz frequency and breathing muscle artefacts[1-3]. These noisy elements have to be removed before the signal is used for next data processing like heart rate frequency detection. Digital filters and signal processing should be designed very effective for next real-time applications in embedded devices. Heart rate frequency is very important health status information. The frequency measurement is used in many medical or sport applications like stress tests or life treating situation prediction. One of possible ways how to get heart rate frequency is compute it from the ECG signal. Heart rate frequency can be detected from ECG signal by many methods and algorithms. Many algorithms for heart rate detection are based on QRS complex detection and hear rate is computed like distance between QRS complexes. QRS complex can be detected using for example algorithms from the field of artificial neural networks, genetic algorithms, wavelet transforms or filterbanks. Moreover the next way how to detect QRS complex is to use adaptive threshold. The direct methods 99

2 for heart rate detection are ECG signal spectral analyze and Short- Term Autocorrelation method[4,5]. Disadvantage of all these methods is their complicated implementation to microprocessor unit for real time heart rate frequency detection. Real time QRS detector and heart rate computing algorithm from resting 24 hours ECG signal for 8-bit microcontroller is described in. This algorithm is not designed for physical stress testwith artefacts. The designed digital filters and heart rate frequency detection algorithms are very simple but robust. They can be used for ECG signal processing during physical stress test with muscle artefacts. They are suitable for easy implementation in C language to microprocessor unit in embedded device. Design of these methods has been very easy with Matlab tools and functions[7,8,9,10]. PROPOSEDSYSTEM ECG signal get from patient by electrodes and give to the controller. Then it process by image processing. Butterworth Notch filter is also used to remove power-line interference of 50 and 100 Hz.Take FFT (Fast Fourier Transform) for frequency domain conversion from time domain to calculate the spectrum of our signal. Developed coding for threshold, peak detection, heart rate and get result shown in fig 1. Figure 1. Block diagram of proposed system This example shows (Fig 2) how to detect the QRS complex of electrocardiogram (ECG) signal in real-time. Model based design is used to assist in the development, testing and deployment of the algorithm.the electrocardiogram (ECG) is a recording of body surface potentials generated by the electrical activity of the heart. Clinicians can evaluate an individual's cardiac condition and overall health from the ECG recording and perform further diagnosis.a normal ECG waveform is illustrated in the following figure [3]. Because of the physiological variability of the QRS complex and various types of noise present in the real ECG signal, it is challenging to accurately detect the QRS complex. 100

3 ECG SignalSource Fig 2. Block diagram for real time QRS detection The ECG signals used in the development and testing of the biomedical signal processing algorithms are mainly from three sources: 1) Biomedical databases (e.g., MIT-BIH Arrhythmia Database) or other pre-recorded ECG data; 2) ECG simulator; 3) Real-time ECG data acquisition.in this example, the following pre-recorded and simulated ECG signals are used. The signals all have sampling frequencies of 360Hz.one set of recorded real ECG data sampled from a healthy volunteer with a mean heart rate of 82 beats per minute (bpm). This ECG data was pre-filtered and amplified by the analog front end before feeding it to the 12 bitadc.four sets of synthesized ECG signals with different mean heart rates ranging from 45 bpm to 220 bpm. ECGSYN is used to generate synthetic ECG signals in MATLAB. Fig 3. ECG signal source A real-time QRS detection algorithm, which references developed in Simulink with the assumption that the sampling frequency of the input ECG signal is always 200 Hz (or 200 samples/s). However the recorded real ECG data may have different sampling frequencies ranging from 200 Hz to 1000 Hz, e.g., 360 Hz. To bridge the different sampling frequencies, a sample rate converter block is used to convert the sample rate to 200 Hz. A buffer block is inserted to ensure the length of the input ECG signal is a multiple of the calculated decimation factor of the sample-rate converter block. 101

4 Real-Time QRS Detection of ECGSignal The QRS detection block detects peaks of the filtered ECG signal in real-time. The detection threshold is automatically adjusted based on the mean estimate of the average QRS peak and the average noise peak. The detected peak is classified as a QRS complex or as noise, depending on whether it is above the threshold. The following QRS detection rules reference the PIC-based QRS detector implemented in. Rule 1: Ignore all peaks that precede or follow larger peaks by less than 196 ms (306bpm). Rule 2: If a peak occurs, check to see whether the raw signal contains both positive and negative slopes. If true, report a peak being found. Otherwise, the peak represents a baseline shift. Rule 3: If the peak is larger than the detection threshold, classify it as a QRS complex. Otherwise classify it as noise. Rule 4: If no QRS has been detected within 1.5 R-to-R intervals, but there is a peak that was larger than half the detection threshold, and that peak followed the preceding detection by at least 360ms, classify that peak as a QRScomplex. Simulate and Deploy 1. Open the examplemodel. 2. Change your current folder in MATLAB to a writablefolder. 3. On the model tool strip, click Run to start the simulation. Observe the Heart Rate display and the raw and filtered ECG signal in the scope, which also illustrates the updating of peaks, threshold and estimated mean heartrate. 4. Open the dialog of ECG Signal Selector block. Select the ECG signal mean heart rate in the drop down menu. Click Apply and observe the real-time detection results in the scopes and Heart Ratedisplay. 5. Click Stop to endsimulation. 6. After selecting target hardware, you can generate code from the ECGSignal Processing subsystem and deploy it to thetarget. Time-DomainAnalysis So far, research in time-domain analysis has been aiming at obtaining roughly estimated ranges of each statistical measure, which might lead to better indicative results for various cardiovascular diseases. In general, time domain measures used for signal analysis include standard deviation of all NN intervals (SDNN) in seconds, standard deviation of the averages of NN intervals in all 5- min segments of the entire recording (SDANN) in milliseconds, the square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) in milliseconds, mean of the standard deviations of all NN 102

5 intervals for all 5-min segments of the entire recording (SDNN index) in milliseconds, standard deviation of differences between adjacent NN intervals (SDSD), and number of pairs of adjacent NN intervals differing by more than 50 ms in the entirerecording.in this regard, three variants are possible, namely, counting all such NN interval pairs, only pairs in which the first or the second interval is longer (NN50 count), or NN50 count divided by the total number of all NN intervals (pnn50) in percentage. The resulting numbers that fall out of the normal case may be due to either lower or higher number of heart beats. Previous studies showed apparent implications of SDNN and pnn50 in patients with chronic heart failure (CHF) and acute myocardial infarction (AMI). Although RMSSD has been preferred over pnn50 due to its robustness, pnn50 indicates cardiovascular risk levels more clearly. Having a value of SDNN that is less than 50 ms or a pnn50 value lower than 3% is regarded as an implication of high risk. In contrast, if SDNN falls between 50 ms and 100 ms, implies moderate risk and having SDNN greater than 100 ms or pnn50 over 3% is considered normal. Most of the time-domain measures are related to the evaluation of cardiovascular high-risklevels.still, however, their beneficial reference data could also be supportive in simplifying the judgment of cardiovascular health state via analyzing HRV at rest. Time-domain measures are characterized by simplicity of calculation, but are not sufficiently informative when utilized in a stand-alone manner. However, frequency-domain and geometry-based analysis techniques do not have reference data such as those of timedomain. Therefore, to maintain a reliable HRV-related study, data interpretation should be performed by combining more than one analysis technique. The validity of normal values of HRV at rest in a young (18 to 25 years old), healthy and active Mexican population was investigated. The argument involved time-domain, frequency- domain analysis methods, and a Poincare plot. A thirty-minute time window of HR recordings is acquired for 200 individuals. Significant variations were found between athletes and active subjects. These major differences, however, were not based on gender. Percentile ranges and distributions were obtained for all predefined population categories that can be used as a reference for future researches. Frequency-DomainAnalysis Time domain measures are easy to compute, but they lack the capability of distinguishing between sympathetic and parasympathetic contributions to HRV. In comparison, frequency domain methods evaluate HRVs by examining the frequency content of each acquired ECG signal. The two main frequencies that resemble ANS activity are the low frequency component (LF) ranging from 0.04 to 0.15 Hz, and the high frequency component (HF) falling between 0.15 and 0.4 Hz. It is established that LF indicates physiologically the sympathetic modulation of heart rate while HF exploits the vagal or parasympathetic activity of ANS and matches the respiratory activities. The index ratio LF/HF describes the sympathovagal balance and shows which part of the ANS is dominating. Each parameter (LH or HF) is computed by integrating with respect to frequency and expressed in units of (ms)2. The result is also expressed in terms of Power Spectral Density (PSD). To obtain LF and HF components, avariety of approaches can be implemented one of which is FFT that transforms the signal to computation of the PSD from the measured parameters. However, FFT is not an adequate approach for analyzing non-stationary signals as previously expressed. As indicated earlier in the text, HHT has the capabilities to handle the FFT shortcomings related to the nonlinear and non-stationary nature of the signal. Initially, EMD 103

6 is applied to the signal, which resolves the signal into smaller IMFs or components. Each obtained IMF is then transformed into its own frequency domains by applying Hilbert transform. However, previous investigations demonstrated that there exists an approximate correlation between time domain and frequency domain measures such that both are related to each other and also contribute to HRVassessment. Butterworthfilter The Butterworth filter is a type of signal processing filter designed to have as flat a frequency response as possible in the passband. Butterworth had a reputation for solving "impossible" mathematical problems. At the time, filterdesign required a considerable amount of designer experience due to limitations of the theory then in use. The filter was not in common use for over 30 years after its publication. Butterworth stated that:"an ideal electrical filter should not only completely reject the unwanted frequencies but should also have uniform sensitivity for the wanted frequencies".such an ideal filter cannot be achieved but Butterworth showed that successively closer approximations were obtained with increasing numbers of filter elements of the right values. EXPERIMENTAL RESULTS The simulation results are in the heart rate calculator in matlab algorithm. In this workmatlab 2013a is used. In this we get the result are using Butterworth filter for removing the noise shown in fig 4. Fig 4. ECG waveform 104

7 Fig 5. ECG FFT waveform Basically ECG signals are received within the sort of time domain. so as to convert it into frequency domain we tend to square measure opting FFT here. The on top of image explains the conversion of signals from time domain to frequency domainshown in fig 5. Figure 6 Total Filtered Signal By exploiting FFT, noise signal plays a significant role in it. So as to get rid of noise signals we have a tendency to are exploitation Butterworth second order filter here. By exploitation 105

8 it, we've got determined clear wave form of our signal shown in fig 6. Figure 7. ECG conditioned result Threshold for QRS detection. Threshold could be a technique distributed for police work the R wave peak. This method is performed by employing a try of threshold limits knownas higher restricted threshold (hth) and lower restricted threshold (lth) shown in fig 7. Fig 8. Peak Detector Once the information has been processed the peaks it ought to be known. Peaks square measure detected as native maxima. The trace curve is that the total of all peak signals. Little peaks will even hide in additional outstanding peaks. With the assistance of peak detector solely able to notice whether or not the patient is Tachycardia or Bradycardia or normal shown in fig

9 Fig 9. Hardware module The higher than kit in the main consists of PIC Microcontroller, TTL logic device, Opto coupler, electrical device and GSM module. The data s that is received from the electrodes goes to the PIC microcontroller then to the TTL logic device then to the Opto coupler and eventually the output are received to the mobile through GSM module shown in fig 9. Fig 10. Hardware output display The electrocardiogram data's area unit collected from the patient supply passes through all the electrical devices and eventually the output gets received through GSM to the the registered mobile number. Hence the higher than image clearly shown the results of the patients shown in fig

10 CONCLUSION Combined use of MATLAB and Simulink is very useful in ECG signal analysis. Different digital filters are used in simulink to remove noise from raw ECG signal. The noise free ECG signal obtained from filter circuit is used as input for ECG analysis to find various intervals and peaks in MATLAB environment. Many works are done in the field of ECG analysis and they involve complicated calculations and hence difficult to design.the algorithm used in this work is very efficient and simple, so it can be easily implemented on ECG signal. In this case the waveform is divided into positive and negative parts and each section is analyzed separately. Various peaks are detected by finding local maxima and minima of the signal and then setting minimum threshold limit for them according to the standard values. The results obtained can be used for clinical diagnosis by the physician and will be very helpful in finding various abnormalities in theheart. This work is done using the Butterworth filter. This Butterworth filter used to remove the noise like interferences and while using electrode that paste also a one type of nose,so avoid this type of noise this project using the Butterworth filter. In future using the electrodes we will get real time ECG signal after that calculate the heart rate.we have to implement in tiny wearable device with low value for all because it is compact device it leads as a transportable device for patients. The foremost aim is power consumption with charge in device. It'll be associate degree easy device. We have a tendency to square measure operating it on to store info in cloud. REFERENCES [1] Aulia A. Iskandar; Reiner Kolla; Klaus Schilling;A wearable 1-lead necklace ECG for continuous heart rate monitoring Wolfram Voelker 2016 IEEE 18th International Conference on e-health Networking, Applications and Services (Healthcommission) [2]E. Aguilar-Pelaez and E. Rodriguez-Villegas, LED power reduction trade-offs for ambulatory pulse oximetry, in IEEE EMBC, Lyon, August [3]Gavin P. Shorten; Martin J. Burke A precision ECG signal generator providing full Lead II QRS amplitude variability and an accurate timing profile 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. [4]N. Kohli, N. K. Verma, and A. Roy, Svm based methods for arrhythmia classification in ecg, in Computer and Communication Technology (ICCCT), 2010 International Conference on. IEEE, 2010, pp [5]S.Z.Mahmoodabadi,A.Ahmadian,M.D.Abolhasani,M.Eslami,andJ.H. Bidgoli. "ECG feature extraction based on multiresolution wavelet transform", Proceedings of IEEE 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp , Jan [6] Dr.AntoBennet, M, Sankaranarayanan S, Ashokram S,Dinesh Kumar T R, Testing of Error Containment Capability in can Network, International Journal of Applied Engineering Research, Volume 9, Number 19 (2014) pp

11 [7]Dr.AntoBennet, M, SankarBabu G, Natarajan S, Reverse Room Techniques for Irreversible Data Hiding, Journal of Chemical and Pharmaceutical Sciences 08(03): , September [8] Dr.AntoBennet, M,Sankaranarayanan S, SankarBabu G, Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis, Journal of Chemical and Pharmaceutical Sciences 08(03): , August [9] Dr. AntoBennet, M,Sankaranarayanan S, SankarBabu G, Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis, Journal of Chemical and Pharmaceutical Sciences 08(03): , August [10] Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, Performance &analysis of automated removal of head movement artifacts in EEG using brain computer interface, Journal of Chemical and Pharmaceutical Research 07(08): , August

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