ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL WORKBENCH

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1 International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 9, Issue 3, May June 2018, pp , Article ID: IJARET_09_03_012 Available online at ISSN Print: and ISSN Online: IAEME Publication ECG SIGNAL ACQUISITION, FEATURE EXTRACTION AND HRV ANALYSIS USING BIOMEDICAL WORKBENCH Arjun Singh Vijoriya and Dr. Ranjan Maheshwari Department of Electronics, Rajasthan Technical University, Kota ABSTRACT This Paper contains the complete process of ECG/EKG signal Acquisition from hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG has the amplification, filtering and conversion of analog ECG data to digital by using Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used for conversion. Now the acquired ECG data is processed and analyzed with biomedical workbench that provides the various features of ECG signal processing. This system is very easy to implement and cost effective. Keywords: 2-Lead ECG System, LabVIEW, ECG Signal Processing Tools, ECG Analysis, Biomedical Workbench. Cite this Article: Arjun Singh Vijoriya and Dr. Ranjan Maheshwari, ECG Signal Acquisition, Feature Extraction and HRV Analysis Using Biomedical Workbench, International Journal of Advanced Research in Engineering and Technology, 9(3), 2018, pp INTRODUCTION Electrocardiogram (EKG/ECG) is used to measure and monitor the heart electrical activities in detail from many years. These electrical details are used to diagnosis the heart conditions. From centuries to till now there is several advanced hardware and software tools have been developed for Electrocardiogram signal acquisition and analysis [1]. The ECG/EKG signal is the graphical representation of heart electrical activities in the form of voltage and current generated during the cardio muscles contraction and relaxation. The generated voltage/current is very small in magnitude and these could be measure from the body skin surface by placing the appropriate ECG electrode. The magnitude of this ECG signal is about few microvolts to 0.5mv. These cardio signals frequency range is between 0.05 to 100 Hertz (Hz). The electrocardiogram recordings in hospitals are increasing with time. However modern ECGs produce digital output, but still plain paper is in use to record the ECG data. Sometimes ECG data of patient become necessary to transfer at another distance place for analysis and paper based data is too much time consuming and also difficult to have record of 84

2 ECG Signal Acquisition, Feature Extraction and HRV Analysis Using Biomedical Workbench patient database for long period. So it is requirement of present time to have the data in digital form in various analysable file formats [2]. We described the complete ECG data acquisition process from hardware to further signal processing software tools in computer system. Hardware having the several stages from ECG signals amplification, filtering, conditioning to analog to digital conversion and software having the real time plotting of ECG signal in LabVIEW and save the plotted data for further usable digital file formats like.txt,.tdms,.tdm,.xlsx for required time duration. The Further biomedical workbench uses the files to analysis of the ECG data; this is having ECG feature extraction and Heat Rate Variability Analyzer tools as the core requirement of recent study for analysis. The main objective of the project work is to design the ECG system which can help the researchers and doctors to acquire and analyse the ECG data in detail with easy and cost effective tools in very less time. 2. SYSTEM DESIGN OVERVIEW This Complete ECG System can be understood by simple block diagram having different stages. Figure 1 System Block Diagram 3. ECG HARDWARE This ECG hardware contains the signal amplification; signal filtering, signal conditioning and ADC. Since the ECG signal is millivolt signal, to amplify it we uses the double stage national instrumentation amplifier with gain of 50 using IC INA126u at first stage and LMC8081 at second stage. Higher gain at first stage could cause low CMRR therefore the gain is applied to successive stages. The Next stage of this hardware 1.3 volt offset to the amplified ECG signal because the Arduino uno having unipolar ADC that clips the negative part of the signal therefore to avoid the clipping of the signal 1.3 volt offset is applied to the signal using the op-amp LMC6081 with unity gain. Figure 2 A front view of ECG hardware connected with arduino uno Above complete setup produced the amplified and shifted clear ECG waveform. Since this signal is in analog form, to make this analog signal into digital format it is connected this to Arduino uno which is having the 6-channel, 10-bit resolution analog to digital convertor. 4. DATA ACQUISITION The Arduino uno board is interfaced with PC through the LabVIEW software for data acquisition from the ECG hardware to personal computer and LabView has the different Arduino modules that can acquire the data continuously at specific sampling rate. This acquire digital data from Arduino uno is stored in.tdms file using write measurement files module of LabView which is having the various functions to store the data file like different 85 editor@iaeme.com

3 Arjun Singh Vijoriya and Dr. Ranjan Maheshwari file extensions and number sample to store and time limit to store the file. The Labview VI for data acquisition with Arduino Uno Interface is shown in figure (3). Figure 3 Labview VI for data acquisition with Arduino Uno Interface This complete setup produces real time ECG signal on computer screen and records this signal into a file format that is used for further analysis. 5. ECG SIGNAL PROCESSING AND FEATURE EXTRACTION Biomedical Workbench of National Instrumentation is used for ECG signal processing since it is having ECG feature extractor and heart rate variability analyzer with various filtering and plotting functionality. ECG feature extractor of Biomedical Workbench is used for filtering and feature extraction of ECG signal. It is able to import ECG signals in different file formats. This tool having robust integrated feature extraction algorithms to detect ECG signal features, such as the QRS Complex, P wave and T wave, total number of beats, Iso level mean and standard deviation, ST level mean and standard deviation, PR Interval mean and standard deviation, QT interval mean and standard deviation. It can save extracted ECG features into TDMS file and also can take print. It transfers Calculated RR interval data to Heart Rate Variability Analyzer of Biomedical Tool Kit for HRV analysis. Below figure shows the feature extraction of 8:46 minute ECG data taken from the above hardware circuit. Figure 4 ECG Feature Extractor of Biomedical Workbench After applying inbuilt signal processing methods, it produces following results with histogram plot of 8:46 minute ecg data. Figure 5 Heart rate histogram and ECG features 86 editor@iaeme.com

4 ECG Signal Acquisition, Feature Extraction and HRV Analysis Using Biomedical Workbench 6. HEART RATE VARIABILITY AND ANALYSIS Heart rate variability (HRV) is actually a physiological occurrence which implies shifts within time interval or space in between a single beat of the cardiovascular system to the subsequent. The inter beat interval (IBI) is most likely the time in between one R-wave to the upcoming. That is acquired in milliseconds. The inter beat interval is extremely variable quantity throughout any period of time. Heart rate variability relies upon three features particularly physical, emotional and psychological. Therefore, the resulting structure of heart rate variability can be described as a joint result from the facets mentioned above. [3] Figure 6 Measurement of HRV from ECG Signal [2] Heart rate variability analyzer of biomedical workbench provides the all parameters and statistics with plot of RR intervals presented in ECG data shown in figure (7). By using this tool we can analyze the HRV using different analysis methods like Poincare Plot, FFT Spectrum Measures, AR Spectrum Measures, STFT Spectrogram, Gabor Spectrogram, Wavelet Coefficient, DFA Plot and Recurrence Map. 6.1 Statistical Parameters As it's just unveiled that HRV is in fact composed by using a number of RRIs. Time domain HRV factors are therefore likely to relate to variations in HRV, in other words, the difference in RRIs. Most significant parameters have been put up in the table. Table 1 Statistical results calculated by Heart Rate Variability Analyzer Statistical Results for 8:46 Description Parameter minute data RR mean Mean of RR intervals 666 ms RR std., Standard Deviation RR intervals 34 ms Heart rate mean Mean of heart rate 90 bpm Heart rate std Standard Deviation of heart rate 5.5 bpm RMSSD Root Mean Square of Successive RR intervals 25 ms NN50 Number of successive RR interval having difference greater than 50 ms. 25 pnn50 It is the portion of NN50 in all RR intervals. 3.2 RR triangular index 9 TINN ms Below figure shows the plot generated by Heart rate Variability Analyzer of National Instrumentation between the extracted RR intervals and number similar RR intervals (Count) for recorded 8.46 minute data editor@iaeme.com

5 Arjun Singh Vijoriya and Dr. Ranjan Maheshwari Figure 7 RR Interval Vs count, plot generated by HRV analyser. All those time domain factors are recognized to possess connection to the health issue of the person and in fact utilized in the recognition of the health problem of sufferers. For instance, those factors will probably have greater value if RRIs tend to be extremely varying that will due to suffering sinus disorder, premature ventricular contraction and atrial fibrillation. Whenever the RRIs possess lesser variation, e.g., third degree AV block7, those factors possess reduced value [4]. Hence by acquiring the difference in the parameters, it may be possible to identify the sudden variations within the body of sufferer just in time that is of good advantageous Poincare Plot Poincar e plot is just a return chart that can help to conduct graphical study of information. We can even add an ellipse into the plot structure by calculating descriptors SD1 (Standard Deviation1), SD2 (Standard Deviation 2) and SD1/SD2 ratio to analyze the reports quantitatively [5]. The Poincare plot provides a very useful visible interaction into the R-R data files by portraying both the short as well as long-term changes within the recording. Study on Poincare plots can be carried out from a quick visual check on the structure of the attractor (such as butterfly structure), that is used to identify the signal. Over chronic renal failure sufferers this method has turned out to be helpful to examine the emergency forecast in the existence of coronary disorder. Still, the evaluation and calibration of such qualitative categories are complicated as they are very highly subjective. An actual quantitative evaluation on the HRV attractor shown over the Poincare plot could be produced by changing it in an ellipse. For this overall performance testing, the SD1, SD2 and region of ellipse utilized as analysis variables. These types of analysis variables have a variety of explanations in other analysis reports [6]. The foremost proper explanations are defined below: SD1: Standard Deviation1 This is actually the standard deviation (SD) of the entire instantaneous beat to beat N-N time interval variability (SD1 or ellipse's minor axis) [6] SD2: Standard Deviation 2 This is actually the standard deviation (SD) of the entire long-term N-N time interval variability (SD2 or ellipse's major axis) [6] Area of Ellipse This is actually the range of region covered up by ellipse. This is determined by performing the multiplication on π, SD1 and SD2. Figure 8 Poincare plot and its generated SD1 and SD2 over 8:46 minute data by Heart rate Variability Analyzer 88 editor@iaeme.com

6 ECG Signal Acquisition, Feature Extraction and HRV Analysis Using Biomedical Workbench 6.3. FFT Spectrum Measures In an attempt to examine the sympathovagal equilibrium, scientific study on the frequency domain has become essential. The PSD commonly utilized to acquire these types of details. For a particular duration of the RR series, the PSD will be calculated very first and some other analysis methods choose to follow. Depending on the proven fact that HRV is varying in accordance with the movements of the person [7]. As things are stated in [8], factors which are found within Low Frequency LF: 0.04 Hz LF < 0:15 Hz and within High Frequency HF: 0.15 Hz HF < 0.4 Hz bands are actually the main aspects those are directly related to the health problem of the individual. Below is the FFT spectrum analysis result generated by Heart Rate Variability Analyzer conducted on previous data at the following setting of frequency band, FFT and window respectively. VLF: Hz; LF: Hz; HF: Hz, Interpolation rate: 2 Hz; Frequency bins: 1024 and window sample length: 1024; overlap: 50%. Table 2 Numerical data of FFT spectrum analysis of normal 8:47 minute ecg signal with and without using windows using Heart Rate Variability Analyzer. Windows selection VLF Power (ms2) LF Power (ms2) HF Power (ms2) VLF (%) LF (%) HF (%) LF norm (n.u.) HF norm (n.u.) LF/HF Corresponding Plot None (a) Hanning (b) Hamming (c) Blackman-Harris (d) Exact Blackman (e) Blackman (f) Flat Top (g) 4 Term B-Harris (h) 7 Term B-Harris (i) Low Sidelobe (j) Figure 9 Plots generated by above FFT spectrum measure between PSD (s^2/hz) and Frequency (Hz) 89 editor@iaeme.com

7 Arjun Singh Vijoriya and Dr. Ranjan Maheshwari 7. CONCLUSION Hardware and LabVIEW software both together creates real time ECG waveform. This real time ECG waveform stored in a digital file at required time duration and sampling rate. Biomedical workbench of National Instrumentation have very efficient tools for ECG signal processing, feature extraction and heart rate variability analysis. All the analysis techniques described above are very advanced and extensively used by researchers. This complete process from getting signal to its heart rate variability analysis is very easy and can be used for self-diagnosis. REFERENCES 1. Raja Brij Bhushan, Ranjan Maheshwari and Amitabh Sharma, Development of simultaneous quantitative ECG system, National Conference on Biomedical Engineering, Roorkee, India, April 21-22, 2000, pp Braunwald E. (Editor), Heart Disease: A Textbook of Cardiovascular Medicine, Fifth Edition, p. 108, Philadelphia, W.B. Saunders Co., ISBN Valerie A. MacIntyre a, Peter D. MacIntyre b & Geoff Carre, Heart Rate Variability as a Predictor of Speaking Anxiety, Vol. 27, No. 4, October December 2010, pp U Rajendra Acharya, K Paul Joseph, N Kannathal, Choo Min Lim, and Jasjit S Suri. Heart rate variability: a review. Medical and Biological Engineering and Computing, 44(12): , Golińska, Agnieszka Kitlas. "Poincaré plots in analysis of selected biomedical signals."studies in logic, grammar and rhetoric 35.1 (2013): Claudia Lerma, Oscar Infante, Hector Perez-Grovas and Marco V.Jose. Poincare plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clinical Physiology & Functional Imaging (2003) 23, pp Axel Schäfer and Jan Vagedes. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. International journal of cardiology, 166(1):15--29, Javier Mateo and Pablo Laguna. Improved heart rate variability signal analysis from the beat occurrence times according to the ipfm model. Biomedical Engineering, IEEE Transactions on, 47(8): , editor@iaeme.com