G-002 R Database and R-Wave Detecting System for Utilizing ECG Data Takeshi Nagatomo Ikuko Shimizu Takeshi Ikeda Akio Sashima Koichi Kurumatani R R MIT-BIH R 90% 1. R R [1] 2 24 16 Tokyo University of Agri. and Tech. 2 24 16 Naka cho, Koganei shi, Tokyo 184 8588, Japan 2-3-26 Advanced Industrial Science and Technology(AIST) 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan [2]-[4] R Medical waveform Format Encoding Rules(MFER)[5] MIT-BIH[6] ABS1[7] R R MIT-BIH 90 R 2 R 3 4 R 5 6 365
2.R R R R Pan-Tompkins(PT) [8] R 2 SQRS[9] PT Q R WQRS[10] Quad Level Vector(QLV)[11] QRS R 10 1.8 Continuous Wavelet Transform(CWT)[12] Mexican hat 4 ψ(t) = (1 2t 2 )e t2 (1) t[s] R R 10s 0.67 Instantaneous HeartRate(IHR)[13] Short-Term AutoCorrelation(STAC)[14] IHR STAC 1 R 3. 3.1. 1 1: 2 1 2 R R 3.2. Relational Database PostgreSQL 4 R ID 4 2 366
1) 2) 3.3. 1)MFER[5] 2)MIT-BIH[6] 3) ABS1[7] MFER MFER MFER MFER 3.4. 2 4.1.R R R 1) 2)R 3) 3 R 1) [15] P QRS T 225 ms 225 ms 450 ms 3 3: ( 1 0.1 mv 1 40 ms 0 mv ( ) ) 2)R R 2: 2 1 1 4. R 1. (R ) 2. 3. 1 4. R 0.03 R 5. 1. 4 367
A B 5.2. QLV CWT 5 8 4: R ( ) R 0.03 R 1 200 R 0.03 3) R R R 10 10s 0.66 R 10 R R 10 0.66 4.2. R R 1 = 5. 60 [/min] (2) 1 [s] R 5.1. R MIT-BIH 48 MIT-BIH R (ground truth) R 5: 1( A 0.7 ( ) 48 QLV 39 CWT 36 ) 6: 2( A 40 ) 7: 3(QLV A 39 ) 368
11: CWT 8: 4(CWT A 36 ) 0.7 9 11 9: ( MIT- BIH 48 ( ) ) R 0.7 0.7 5.3. R 2 A R P T Q S R R 12 14 10: QLV 12: 1( R ( ) ) 369
B 7 16 22 13: 2 16: 1 14: 3 R 10 R 10 R 15 17: 2 18: 3 15: 5( ) CWT R Q S T 15 R Q T R R R T R 16 18 R Q S 1 R Q S 18 Q R R 1 R 0.03 19: 4 370
20: 5 19 20 R R ( ) R R 21: 6 21 1 2 R 1 21 2 3 MIT-BIH R R [1] DscyOffice (http://dscyoffice.net/ office/law/010320.htm) [2] (http: //www.omron-portable-ecg.jp/medical/ portable-ecg/software_pag_index.php) [3] (http://www. fukuda.co.jp/medical/products/ecg/) [4] (http://www. nihonkohden.co.jp/iryo/products/physio/ 01_ecg/) [5] (http://www.mfer.org/ jp/whatismfer.htm) [6] Ary L. Goldberger, et al. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101,23, e215 e220 (2000) 22: 7 22 R R 10 R R 6. [7],,,.. IT 7, Vol.8, No.1, 54 57 (2013) [8] Jiapu Pan and Willis J. Tompkins. A realtime QRS detection algorithm. IEEE Transactions on Biomedical Engineering, Vol.3, 230 236 (1985) [9] PhysioNet WFDB Applications, sqrs (http://www.physionet.org/physiotools/ wag/sqrs-1.htm) [10] PhysioNet WFDB Applications, wqrs (http://www.physionet.org/physiotools/ wag/wqrs-1.htm) 371
[11] Hyejung Kim et al. ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans. on Information Technology in Biomedicine, Vol.14, No.1 93 100 (2010) [12] Inaki Romero, et al. Low-power robust beat detection in ambulatory cardiac monitoring. Proc. IEEE Biomedical Circuits and Systems Conference (2009) [13] Masanao Nakano, et al. Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems. Proc. Engineering in Medicine and Biology Society (2012) [14] Shintaro Izumi, et al. Low-power hardware implementation of noise tolerant heart rate extractor for a wearable monitoring system. Proc. IEEE International Conference on Bioinformatics and Bioengineering (2013) [15] Wenli Chen, et al. Detection of QRS complexes using wavelet transforms and golden section search. Proc. International Conference on Intelligent Systems and Knowledge Engineering (2007) 372