Automated analysis of the auditory brainstem response using derivative estimation wavelets.
In this paper, we describe an algorithm that automatically detects and labels peaks I-VII of the normal, suprathreshold auditory brainstem response (ABR). The algorithm proceeds in three stages, with the option of a fourth: (1) all candidate peaks and troughs in the ABR waveform are identified using zero crossings of the first derivative, (2) peaks I-VII are identified from these candidate peaks based on their latency and morphology, (3) if required, peaks II and IV are identified as points of inflection using zero crossings of the second derivative and (4) interpeak troughs are identified before peak latencies and amplitudes are measured. The performance of the algorithm was estimated on a set of 240 normal ABR waveforms recorded using a stimulus intensity of 90 dBnHL. When compared to an expert audiologist, the algorithm correctly identified the major ABR peaks (I, III and V) in 96-98% of the waveforms and the minor ABR peaks (II, IV, VI and VII) in 45-83% of waveforms. Whilst peak II was correctly identified in only 83% and peak IV in 77% of waveforms, it was shown that 5% of the peak II identifications and 31% of the peak IV identifications came as a direct result of allowing these peaks to be found as points of inflection.
Cooperative Research Centre for Sensor Signal and Information Processing (CSSIP), School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
Andrew P
AP
Bradley
Wayne J
WJ
Wilson
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016057:Evoked Potentials, Auditory, Brain Stem / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D011930:Reaction Time / Q000502:physiology*