5.3 Experimental Results
5.3.2 Architecture
0 0.5 1 1.5 2 2.5
1 41 81 121 161 201 241 281 321 361 401 441 481 521
h_algorithm h_netlist
Figure 5.2: Comparison of Hurt exponents obtained from algorithm and architecture(synthesized netlist)
Table 5.3: Confusion matrix for synthesized netlist th=1.5520 Synthesized Netlist Label/Classified
Output Normal Abnormal
Normal TN FP
Abnormal FN TP
The proposed architecture in chapter 4 has been synthesized usingU M C130nmtechnology. The synthesized netlist occupies 0.14mm2of area and 26nW of power when processing is done at 1kHz.
Comparison of proposed design and the state of the art technique [26] has been presented in table
5.4. The proposed design takes much less area(almost 6 times less) and low power(almost 6 times) compared to state of the art technique. This makes it suitable for resource constrained environment like IoT and Remote Health Care, where low power and less area of utmost need. Fig.5.2 shows
Table 5.4: comparison of synthesized designs Parameters/Design State of the art Proposed
Area (mm sq.) 0.7 0.14
Power (nW @ 1khz) 182.94 26
the plot of hurst exponent obtained for different cases through algorithm and proposed architecture.
First 280 frames in Fig. 5.2 correspond to various abnormalities and the next 300 correspond to normal sinus rhythm. Table 5.3. Shows confusion matrix for synthesized netlist. The sensitivity 94.61% and specificity 99.66%of synthesized netlist are respectively. The sensitivity obtained by architecture is slightly less than the algorithm. The reason behind this loss is the finite word length effect. Algorithm is designed and tested in floating point environment using Matlab as a tool, while architecture has been designed by converting floating point number to 16 bit fixed point representation. The reason of using fixed point representation is its low complex, low power and ease of implementation. The loss in accuracy using 16 bit fixed point representation is very less which can be ignored with respect to benefit of low power and ease of implementation.
Hardware Validation
The proposed Hardware design methodology has been synthesized on FPGA xilinx spartan 6 plat- form for Hardware validation. Raw ECG samples downloaded from MIT-BIH database were pro- cessed as mentioned in section 1 of this chapter and then stored in ROM of FPGA. The design was configured to take samples automatically from ROM. The output ports of the design were diverted to output leds of the board. To take full picture of IO’s activity we utilized chipscope functionality of xilinx FPGA. Chipscope puts extra monitoring hardware on the FPGA along with Design so that IO’s activity can be properly captured and displayed on the PC.
Figure 5.3: Hardware Validation of Classifier
Fig.5.3 shows the snapshot of IO’s activity of classifier. Classifier has three input ports re- set,enable in and data in.“reset” acts as active high signal for resetting the design . “enable” is active high signal for enabling the design. “data in” is feed input ECG samples in the design. Clas- sifier has three output ports e.g. enable out, h value and classified result. ”h value” gives the hurst value of samples in reduced format for debugging purpose. ”enable out” is handshaking signal for interfacing classifiers with other blocks. “classified result” gives classified result i.e. zero for normal signals and one for abnormal signals. In Fig.5.3 we can see the toggling of “classified result” pin when abnormal samples are fed.
Chapter 6
Conclusion and Future Work
The presented works attempts to use Hurst exponent for implementation of low complex and low power architecture for E.C.G classification. Although there are many ECG classification algorithms available in literature discussed in chapter 1, but as per our our literature survey we found very few to be implementable in resource constrained environment. The primary reason for this lagging is the use of complex DSP and artificial intelligence based algorithm.
Here in this work we have tried to use the simplicity of Hurst exponent and Haar wavelet to present the binary classification of ECG signal. Standard Hurst exponent method has been fine-tuned for ECG classification such that its accuracy increases. Not only this the modification also reduces the Haar wavelet decomposition of signal to 6 levels only which increases the speed of operation and decreases power consumption which is a positive sign and is very useful for modern day IOT and body sensor network technology. The presented method do not need any scale adjustment for different ADC environment, so its versatile in nature. Although here we have targeted four MITBIH database but since this approach depends on morphology of signal and not on absolute value of collected signal, so it can be targeted for other heart anomalies as well.
References
[1] World Health Organization, Cardiovascular diseases (CVDs), Fact sheetN0317, March 2013
[2] 2012 european union cvd report http://www.escardio.org/about/what/advocacy/EuroHeart/Pages/2012- CVD-statistics.aspx %beginthebibliography1
[3] Gaurav, Kumar,Bhise, Manoj,Gudapati, Ramesh, Gupta, Sushil”Emerging risk factors for car- diovascular diseases: Indian context”2013/9/1 Indian Journal of Endocrinology and Metabolism [4] V. Mahesh, A. Kandaswamy, C. Vimal, B. Sathish, ECG arrhythmia classification based on logistic model tree, Journal of Biomedical Science and Engineering, Vol.2, No.6, pp.405-411, October 2009
[5] S. Palreddy, Y. H. Hu, V. Mani, and W. J. T ompkins (1997) A multipleclassifier architecture for ECG beat classification, IEEE Workshop on Neural Network for Signal Processing, 172181.
[6] B. Mohammadzadeh-Asl and S. K. Setarehdan, (2006) Neural network based arrhythmia classi- fication using heart rate variability signal, Proceedings of the EUSI- PCO.
[7] J. Lee, K. L. Park, M. H. Song, and K. J. Lee, (2005) Arrhythmia classification with reduced fea- tures by linear discriminant analysis, IEEE EMBS 2005, 27th Annual International Conference, Engineering in Medicine and Biology Society, 11421144.
[8] Saiveena, K.; Gupta, P.; Amarnath, M.; Sastry, C.S., ”Characterization of ECG signals us- ing multiscale approach,” Signal Processing and Communications (SPCOM), 2012 International Conference on , vol., no., pp.1,5, 22-25 July 2012
[9] Bollepalli S. Chandra, Challa S. Sastry, Soumya Jana, Telecardiology: Hurst Exponent based AnomalyDetection in Compressively Sampled ECG Signals IEEE healtcom 2013 international conference.
[10] Sheng Hu; Zhenzhou Shao; Jindong Tan, ”A Real-Time Cardiac Arrhythmia Classification System with Wearable Electrocardiogram,” Body Sensor Networks (BSN), 2011 International Conference on , vol., no., pp.119,124, 23-25 May 2011
[11] Imah, E.M.; Al Afif, F.; Fanany, M.I.; Jatmiko, W.; Basaruddin, T., ”A comparative study on Daubechies Wavelet Transformation, Kernel PCA and PCA as feature extractors for arrhythmia detection using SVM,” TENCON 2011 - 2011 IEEE Region 10 Conference , vol., no., pp.5,9, 21-24 Nov. 2011 doi: 10.1109/TENCON.2011.6129052
[12] Liang-Yu Shyu; Ying-Hsuan Wu; Hu, W., Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG, Biomedical Engineering, IEEE Transactions on, vol.51, no.7, pp.1269-1273, July 2004. doi:10.1109/TBME.2004.824131
[13] Ceylan, R. and zbay, Y. 2007. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Syst. Appl. 33, 2 (Aug. 2007),286-95..
DOI=http://dx.doi.org/10.1016/j.eswa.2006.05.014
[14] Elsayad, A.M.;, Classification of ECG arrhythmia using learning vector quantization neural networks, Computer Engineering & Systems, 2009. ICCES 2009. International Conference on , vol., no., pp.139-144, 14-16 Dec. 2009 doi: 10.1109/ICCES.2009.5383295.
[15] KarstenSternickel, Automatic pattern recognition in ECG time series, Computer Methods and Programs in Biomedicine, Vol- ume 68, Issue 2, May 2002, Pages 109-115, ISSN 0169-2607, DOI:
10.1016/S0169-2607(01)00168-7.
[16] Karraz, G.; Magenes, G.;, Automatic Classification of Heartbeats using Neural Network Classi- fier based on a Bayesian Framework, Engineering in Medicine and Biology Society, 2006. EMBS 06. 28th Annual International Conference of the IEEE , vol., no., pp.4016-4019, Aug. 30 2006- Sept. 3 2006 doi:10.1109/IEMBS.2006.259356
[17] Karraz, G.; Magenes, G.;, Automatic Classification of Heartbeats using Neural Network Classi- fier based on a Bayesian Framework, Engineering in Medicine and Biology Society, 2006. EMBS 06. 28th Annual International Conference of the IEEE , vol., no., pp.4016-4019, Aug. 30 2006- Sept. 3 2006 doi:10.1109/IEMBS.2006.259356
[18] Exarchos, T. P., Tsipouras, M. G., Exarchos, C. P., Papaloukas, C., Fotiadis, D. I., and Michalis, L. K. 2007. A methodology for the automated creation of fuzzy expert systems for ischaemic and
arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif,Intell.
Med. 40, 3 (Jul. 2007), 187-200. DOI= http://dx.doi.org/10.1016/j.artmed.2007.04.001.
[19] Yeh Y.-C., Wang W.-J., Chiou C.W.;, Heartbeat case determination using fuzzy logic method on ECG signals, International Journal of Fuzzy Systems, Vol. 11, No. 4, December 2009 [20] Hao Zhang; Li-Qing Zhang;, ECGanalysisbasedon PCA and Support VectorMachines, Neural
Networks and Brain, 2005. ICNN&B 05. International Conference on , vol.2, no., pp.743-747, 13-15 Oct. 2005 doi: 10.1109/IC- NNB.2005.1614733
[21] Kampouraki, A.; Manis, G.; Nikou, C.; , Heartbeat Time Series Classification With Support Vector Machines, Information Technology in Biomedicine, IEEE Transactions on, vol.13, no.4, pp.512-518, July 2009 doi: 10.1109/TITB.2008.2003323.
[22] Nasiri, J.A.; Naghibzadeh, M.; Yazdi, H.S.; Naghibzadeh, B.;, ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm, Computer Modeling and Simulation, 2009. EMS 09. Third UKSimEuropean Symposium on, vol., no., pp.187-192, 25-27 Nov. 2009 doi: 10.1109/EMS.2009.39 .
[23] Hamid Khorrami, MajidMoavenian, A comparative study of DW, CWT and DCT transforma- tions in ECG arrhythmias classification, Elsevier, Expert Systems with Applications 37 (2010) 57515757.
[24] Mazomenos, E.B.; Biswas, D.; Acharyya, A.; Taihai Chen; Maharatna, K.; Rosengarten, J.;
Morgan, J.; Curzen, N., ”A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications,” Biomedical and Health Informatics, IEEE Journal of , vol.17, no.2, pp.459,469, March 2013 doi: 10.1109/TITB.2012.2231312
[25] Murthy, Vrudhula K.; Grove, T.M.; Harvey, G.A.; Haywood, L. Julian, ”Clinical Usefulness Of ECG Frequency Spectrum Analysis,” Computer Application in Medical Care, 1978. Proceedings.
The Second Annual Symposium on , vol., no., pp.610,612, 5-9 Nov 1978
[26] Taihai Chen; Mazomenos, E.B.; Maharatna, K.; Dasmahapatra, S.; Niranjan, M., ”Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems,” Emerging and Selected Topics in Circuits and Systems, IEEE Journal on , vol.3, no.1, pp.75,85, March 2013 Application in Medical Care, 1978. Proceedings. The Second Annual Symposium on , vol., no., pp.610,612, 5-9 Nov 1978