Automated algorithm of automated auditory brainstem response for neonates

신생아 청성뇌간 반응의 자동 판독 알고리즘

  • Jung, Won-Hyuk (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Hong, Hyun-Ki (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Nam, Ki-Chang (National Institute of Advanced Industrial Science and Technology (AIST)) ;
  • Cha, Eun-Jong (Dept. of Biomedical Engineering, College of Medicine , Chungbuk National University) ;
  • Kim, Deok-Won (Dept. of Medical Engineering, College ofMedicine , Yonsei University)
  • 정원혁 (연세대학교 생체공학협동과정) ;
  • 홍현기 (연세대학교 생체공학협동과정) ;
  • 남기창 ;
  • 차은종 (충북대학교 의과대학 의공학교실) ;
  • 김덕원 (연세대학교 의과대학 의학공학교실)
  • Published : 2007.01.25

Abstract

AABR(automated auditory brainstem response) test is used for the screening purpose of hearing ability of neonates. In this paper, algorithm using Rolle's theorem is suggested for automatic detection of the ensemble averaged ABR waveform. The ABR waveforms were recorded from 55 normal-hearing ears of neonates at screening levels varying from 30 to 60 dBnHL. Recorded signals were analyzed by expert audiologist and by the proposed algorithm. The results showed that the proposed algorithm correctly identified latencies of the major ABR waves (III, V) with latent difference below 0.2 ms. No significant differences were found between the two methods. We also analyzed the ABR signals using derivative algorithm and compared the results with proposed algorithm. The number of detected candidate waves using the proposed algorithm was 47 % less than that of the existing one. The proposed method had lower relative errors (0.01 % error at 60dBnHL) compared to the existing one. By using proposed algorithm, clinicians can detect and label waves III and V more objectively and quantitatively than the manual detection method.

자동화 청성뇌간반응검사(automated auditory brainstem response; AABR)는 ABR 파형을 자동으로 검출하여 신생아의 청각선별검사에 사용되고 있다. 본 논문은 앙상블 평균된 ABR 파형에 대해서 롤의 정리를 이용한 새로운 자동화 ABR 파형 검출 알고리즘을 제안하였다. 정상 청력을 가진 신생아의 55개의 귀를 대상으로 30, 40, 50, 60 dBnHL의 다양한 강도를 가진 클릭 자극음에 대한 청성뇌간반응 파형을 측정하였다. 수동 검출법(manual detection method)과 제안된 자동 검출법을 이용하여 파형 III 과 V의 평균 잠복기(average latency time) 차를 분석하였는데, 동일한 파형(잠복기 차 < 0.2 ms)으로 관측되어 두 방법 간에는 유의한 차이가 없었다. 또한 미분 자동 검출법(automated detection method using derivative estimation)과 제안된 자동 검출법을 파형 III과 V로 판명될 후보 파형의 개수에 대해 비교하였다. 미분 자동 검출법에 비해 제안한 자동 검출법에서 후보 파형의 개수가 47 % 감소되어 검출되었다. 또한 수동 검출법에 대한 제안된 자동 검출법의 잠복기 오차율은 미분 자동 검출법에 비해 60 dBnHL의 자극강도에서 낮은 잠복기 오차율(<0.01 %)을 보였다. 따라서 제안된 알고리즘으로 청각전문가가 기존의 수동 검출 방법보다 객관적이고 정량적으로 파형 III과 V를 검출하고 표시할 수 있게 된 데에 의의가 있다.

Keywords

References

  1. L.K. Stein, T. Jabaley, R. Spitz, D. Stoakley, T. McGee, 'The hearing-impaired infant: patterns of identification and habilitation revisited', Ear Hear., Vol. 11, pp. 201-205, 1990 https://doi.org/10.1097/00003446-199006000-00006
  2. J. Coplan, 'Deafness: ever heard of it? Delayed recognition of permanent hearing loss', Pediatrics, Vol. 79, pp. 206-213, 1987
  3. Markides, 'Age at fitting of hearing aids and speech intelligibility', Br. J. Audiol., Vol. 20, pp. 165-167, 1986 https://doi.org/10.3109/03005368609079011
  4. C. Yoshinaga-Itano, A.L. Sedey, D.K. Coulter, A.L. Mehl, 'Language of early- and later-identified children with hearing loss', Pediatrics, Vol. 102, pp. 1161-1171, 1998 https://doi.org/10.1542/peds.102.5.1161
  5. H.M. Robinshaw, 'Early intervention for hearing impairment: differences in the timing of communicative and linguistic development', Br. J. Audiol., Vol. 29, pp. 314-334, 1995
  6. M.L. Apuzzo, C. Yoshinaga-Itano, 'Early identification of infants with significant hearing loss and the Minesota Child Development Inventory', Semin. Hear., Vol. 16, pp. 124-137, 1995 https://doi.org/10.1055/s-0028-1083710
  7. James W. Hall III, 'Handbook of Auditory Evoked Responses', Allyn & Bacon, 1992
  8. R. Goldstein, W. M. Aldrich, 'Evoked Potential Audiometry', Allyn & Bacon, 1999
  9. Andrew P.Bradley, Wayne J.Wilson, 'Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets', Audiol Neurootol, Vol. 10, pp. 6-21, 2005 https://doi.org/10.1159/000081544
  10. Delgado RE, Ozdamar O, 'Automated auditory brainstem response interpretation', IEEE Eng Med Biol Mag, Vol. 2, pp. 227-237, 1994 https://doi.org/10.1109/51.281682
  11. Pratt H, Urbach D, Bleich, N, 'Auditory brainstem evoked potentials peak identification by finite impulse response digital filters', Audiology, Vol. 28, pp. 272-283, 1989 https://doi.org/10.3109/00206098909081634
  12. Woodworth W, Reisman S, Fontaine Ba, 'The detection of auditory evoked response using a matched filter', IEEE Trans Biomed Eng, Vol. 30, pp. 369-376, 1983 https://doi.org/10.1109/TBME.1983.325036
  13. Vannier E, Adam O, Karasinski P, Ohresser M, Motsch J, 'Computer-assisted ABR interpretation using automatic construction of the latency-intensity curve', Audiology, Vol. 40, pp. 191-201, 2001 https://doi.org/10.3109/00206090109073114
  14. Vannier E, Adam O, Motsch J, 'Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels', Artif Intel Med, Vol. 25, pp. 283-301, 2002 https://doi.org/10.1016/S0933-3657(02)00029-5
  15. Alpsan D, Towsey M, Ozdamar O, Tsoi A, Ghista Dn, 'Determining hearing threshold from brain stem evoked potentials. Optimising a neural network to improve classification performance', IEEE Eng Med Biol Mag, Vol. 4, pp. 465-471, 1994 https://doi.org/10.1109/51.310986
  16. Tian J, Juhola M, Gronfors T, 'Latency estimation of auditory brainstem response by neural networks', Artif Intell Med, Vol. 10, pp. 115-128, 1997 https://doi.org/10.1016/S0933-3657(97)00389-8
  17. Popescu M, Papadimitriou S, Karamitsos D, Bezerianos A, 'Adaptive denoising and multiscale detection of the V wave in brainstem auditory evoked potentials', Audiol Neuro Otol, Vol. 4, pp. 38-50, 1999 https://doi.org/10.1159/000013818
  18. Gabriel S, Durrant JD, Dickter AE, Kephart JE, 'Computer identification of waves in the auditory brain stem evoked potentials', Electroencephalogr Clin Neurophysiol, Vol. 49, pp. 421-423, 1980 https://doi.org/10.1016/0013-4694(80)90240-0
  19. Fridman J, John ER, Bergelson M, Kaiser JB, Baird HW, 'Application of digital filtering and automatic peak detection to brain stem auditory evoked potential', Electroencephalogr Clin Neurophysiol, Vol. 53, pp. 405-416, 1982 https://doi.org/10.1016/0013-4694(82)90005-0
  20. Pool KD, Finitzo T, 'Evaluation of a computer-automated program for clinical assessment of the auditory brain stem response', Ear Hear, Vol. 10, pp. 304-310, 1989 https://doi.org/10.1097/00003446-198910000-00006
  21. Boston RJ, 'Automated interpretation of brainstem auditory evoked potentials: A prototype system', IEEE Trans Biomed Eng, Vol. 36, pp. 528-532, 1989 https://doi.org/10.1109/10.24254
  22. Sininger YS, Hyde M, Don M, 'Power-optimized Cumulative, Sequential Statistical Method for detection of auditory evoked potentials using point optimized variance ratio', US Patent 6,196,977, March 2001
  23. BIO-LOGIC System Corp., 'Evoked Protential User's Manual', Rev. 1, pp. E6-E7, 1993