Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
Extraction of a Respiration Rate from ECG Signal using Discrete Wavelet Transform during Exercise Vishakha Khambhati1 & Mitul B. Patel2 1,2
Department of Biomedical Engineering Government Engineering College, Gandhinagar Gujarat, India Abstract: Respiration Rate is a vital parameter which gives an indication about person’s normal and abnormal respiratory illness. Respiratory activity is obtained directly utilizing strain gauge or piezoelectric transducer attached to the subject’s chest or abdomen, nasal thermistor to quantify nasal air pressure, spirometer, plethysmography & Impedance pneumotachometry etc. These direct methods which can be used to measure respiration rate but they have certain drawbacks. They can’t be used in all conditions like ambulatory monitoring, stress condition and also during sleeping condition. The noble aim behind this paper is to extract respiratory information from ECG signal using Discrete Wavelet Transform during Exercise. In that, subjects performing exercise on treadmill according to Bruce protocol upto 9 minute. This method decomposes ECG signal upto 10th level to produce a signal similar to the original respiratory signal & reconstruct the detail components to estimate respiratory signal. The algorithm results were compared to the actual respiration signal using thermistor. The results show that algorithm is able to reconstruct the respiratory waveform & it gives accuracy upto 85% which is acceptable. Keywords: Respiration Rate, Strain guage, Piezoelectric transducer, Spirometer, Nasal Thermistor, Impedance Pneumotachometry, Plethysmography, ECG.
1. Introduction Human body is a complex integration of a number of physiological systems. These systems are dependent of each other and they are mutually correlated. Heart rate is affected by normal respiration due to coupling and interactions existing between cardiorespiratory systems. For that, signal processing methods can be used to derive information regarding heart rate and respiration rate from ECG Signal.
Imperial Journal of Interdisciplinary Research (IJIR)
Respiration is defined as the movement of O2 from the outside air to the cells within tissues and expelling CO2 out [1]. Person breathing rate can be determined by counting the number of times the chest elevates or falls minutely. This signal is mostly utilized for monitoring the patient condition, regarding any respiratory disorders. Conventionally, respiratory signal is recorded by direct techniques like spirometer, pneumography, or plethysmography. But, they have certain drawbacks in many applications such as ambulatory monitoring, stress condition, and sleep studies. So that the combined study of respiratory and cardiac activity suggests indirect methods to derive the respiratory signal. Several signal processing techniques are used to estimate respiratory information from the ECG; its called ECG derived respiratory (EDR) information. Advantages of such methods are its low cost, high accommodation, and the facility to simultaneously monitor cardiac and respiratory activity.
2. Discrete Wavelet Transform Method The Discrete wavelet transform decomposition method consists of low pass filters and high pass filters that divide total signal into two components with half of the original information. In Discrete wavelet transforms, a LPF and a HPF that decompose the signal into two different scales. LPF Coefficients are referred to as “approximation components” and HPF Coefficients are referred to as “detail components”. The decomposition process is repeated on the first approximation signal to produce a second approximation and detail signal [2]. The approximated signal may be passing to the next stage for further decomposition process by breaking the signal content into many levels of lower resolution components, resulting with a multi-level decomposition. Consequently, the decomposition process is iteratively giving a wavelet decomposition
Page 1238
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in tree. A block diagram of three level wavelet decomposition tree is shown in Figure 1, where the approximate coefficients are referred as Aj and detailed coefficients are referred as Dj. The trend of the input signal is the slow-varying part of the signal that mainly contributes to the approximation coefficients which applies the following steps to implement the detrend function. 1. Discrete wavelet transform (DWT) is applied on the ECG signal. 2. Approximation coefficients are set to zero. 3. Then reconstructs the signal based on detail coefficients.
A. Flow Diagram of EDR using DWT Method Flow diagram of respiratory signal derived from ECG signal using DWT method is shown in Figure 2. Basic steps to follow the flow diagram are: 1. 2.
3. 4.
5. 6. 7.
Subjects performed Exercise on treadmill upto 9 minute. During Exercise, actual respiration signal using thermistor and ECG were recorded upto 9 minute. Reference Respiratory Signal is denoised using bandpass filter in MATLAB software. Apply DWT method on ECG Signal in which ECG signal is decomposed upto 10th level and then reconstruct the detail components. So that, estimated respiratory signal is obtained. Next, apply rectification on estimated respiratory signal for peak detection. Calculate Respiration rate based on peak. Compare actual vs. estimated respiration rate.
Figure 1.Wavelet Decomposition Tree [2] Detrend signal is extracted from the ECG signal using threshold frequency acquired by applying below equation.
Where, t = Sampling Duration, and N = No. of samples Trend level specifies the number of levels of the wavelet decomposition, which is approximately,
No. of decomposition level = (1-trend level) * log2 (N)
3. EDR Signal Evaluation Respiration signal is estimated from ECG signal using Discrete Wavelet Transform (DWT) method. This method decomposed ECG signal upto 10th level and then reconstructing detail components or by removing approximation components to obtain a signal similar to the actual respiratory signal.
Imperial Journal of Interdisciplinary Research (IJIR)
Figure 2. Flow Diagram of EDR using DWT Method B. Implementation of EDR using DWT Method DWT method is used to extract respiration rate from ECG signal and comparison is done based on actual vs. estimated respiration rate. Page 1239
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in Figure 3 shows that implementation of estimation of respiratory signal from ECG using DWT method.
Figure 3. Implementation of EDR using DWT Method C. Comparison of Actual Respiratory Waveform
and
Estimated
ECG derived respiration signal is obtained using Discrete Wavelet Transform (DWT) method. Visual inspection showed good similarity between the derived signals and actual respiration signal. Figure 4 shows that EDR methods plotted against the actual respiration signal.
Figure 4. Comparison of actual vs. estimated respiratory waveform
4. Results and Discussion
Table 1. Result assessment between actual and estimated respiration rate Figure 5 shows that comparison of extracted and acquired respiration rate for different 15 subjects upto 0-1 & 1-2 min.
Figure 5. Result analysis of actual vs. extracted RR for 15 subjects
Table 1 shows the mutual relationship between the extracted respiration rate (ERR) and actual respiration rate (ARR) for different subjects. Table 1 also includes the absolute error with the actual respiration signal as a reference. In the current study, reference respiration and derived respiration are the two random variables to be compared. DWT gives an accuracy of 86.71% upto 0-1 min and accuracy of 81.55% upto 1-2 min respectively.
5. Conclusions
The algorithm result shows an average error of 13.28% upto 0-1 min and 18.44% upto 1-2 min. This error is acceptable in terms of respiration rate estimation.
Future work should be include implementation of another method like based on amplitude of R peak and Homomorphic filter for obtaining respiratory activity in order to validate the performance of algorithms during exercise.
Imperial Journal of Interdisciplinary Research (IJIR)
The results of the current study means ECG derived respiration signal using Discrete Wavelet Transform (DWT) method is acceptable. It gives accuracy upto 80-85%. EDR accurately measures the frequency of respiratory efforts, but it does not follow the changes in tidal volume.
6. Future Work
Page 1240
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
7. References [1]
[2]
[3]
[4]
[5] [6]
[7] [8]
[10]
[11]
[12]
[13]
Ambekar MR, Prabhu S. A Novel Algorithm to Obtain Respiratory Rate from the PPG Signal. Int J Comput Appl. 2015;126(15):9758887.http://www.ijcaonline.org/research/ volume126/number15/ambekar-2015-ijca-906263.pdf. Santo AE, México CE De, Lago C, Km DG. Respiration Rate Extraction from ECG Signal via Discrete Wavelet Transform. IEEE. 2010. Yi WJ, Park KS. Derivation of respiration from ECG measured without subject’s awareness using wavelet transform. Proc Second Jt 24th Annu Conf Annu Fall Meet Biomed Eng Soc [Engineering Med Biol. 2002;1:183-184. doi:10.1109/IEMBS.2002.1134420. Campolo M, Labate D. ECG-derived respiratory signal using Empirical Mode Decomposition.(MeMeA), 2011 IEEE 2011;(1):399-403. doi:10.1109/MeMeA.2011.5966727. Alfoouri M, Daqrouq K. ECG signal denoising by Wavelet transform thresholding. Am J Appl Sci. 2008;5(3):276-281. Chouakri SA, Bereksi-Reguig F, Ahmaïdi S, Fokapu O. Wavelet denoising of the electrocardiogram signal based on the corrupted noise estimation. Comput Cardiol. 2005;32:1021-1024. doi:10.1109/CIC.2005.1588284. Kalda RS, Deore PJ. ECG Denoising using Wavelet Transform. 2014;2(7):1689-1692. Mehta P, Kumari M, Professor A. Qrs Complex Detection of Ecg Signal Using Wavelet Transform. Int J Appl Eng Res. 2012;7(11):973-4562. http://www.ripublication.com/ijaer.htm. Daimiwal N, Sundhararajan M, Shriram R. Respiratory rate, heart rate and continuous measurement of BP using PPG. Int Conf Commun Signal Process ICCSP 2014 - Proc. 2014:999-1002. doi:10.1109/ICCSP.2014.6949996. Balaji KP, Jatti A. PPG Signal for Extraction of Respiratory Activity and HR Monitoring of CHF Patients. Int J Adv Res Comput Sci Softw Eng. 2014;4(1):2277-128. BehzadGhanavati, “QRS Detector Circuit”, 2nd International Conference on Electrical, Electronics and Civil Engineering (ICEECE'2012) Singapore April 28-29, 2012. Abdul QayoomBhat, Vineet Kumar, Sunil Kumar, “Design of ECG Data Acquisition System”, International Journal of Advanced Research in Computer Science and Software Engineering,Volume 3, Issue 4, April 2013.
Imperial Journal of Interdisciplinary Research (IJIR)
Page 1241