Nobuko Ikawa

AUTOMATED AVERAGING OF AUDITORY EVOKED RESPONSE WAVEFORMS USING WAVELET ANALYSIS

The auditory brainstem response (ABR) is widely used as an index to assist hearing and brain function diagnoses. In particular, in clinical applications, the rapid detection of ABR peak characteristics is required. One approach to improving the speed of detection is to decrease the number of signal averaging procedures while denoising during the detection of ABR waveforms; another approach is to extract the characteristics of ABR waveform components. In our previous study, to represent ABR waveform components, we obtained not only the frequency characteristics of an ABR but also the frequency characteristics of each component of the ABR based on the time (latency). Using a one-dimensional discrete wavelet transform (DWT) in this latency-frequency analysis, we described an approximate method of reproducing ABR signals with a low SNR from observed values obtained with a smaller number of averaging procedures. At the same time, using this multiple-level frequency decomposition of ABR signals according to the known frequency content of the ABR, we extracted the peak latency of the fast component of the ABR using fewer averagings of the ABR data. From these decomposition and reconstruction results for ABR signals, we proposed the optimal decomposition level of the ABR and explained how we used the waveform of the ABR reconstructed by the inverse DWT (IDWT). In this paper, we propose a method of automated averaging of the ABR using the waveform reconstructed by discrete wavelet multiresolution analysis (MRA). Our proposed method will be useful for the fast detection of ABR latency characteristics in hearing screening test.

The detection of the relation of the stimulus intensity-latency of auditory brainstem response using optimal wavelet analysis

In clinical applications, the rapid detection of the auditory brainstem response (ABR) peak characteristics is required. The input stimulus intensity influences the peak latency of the ABR waveform. In the sound stimulus intensity-latency curve (called I-L curve), the fifth peak (which is called wave V) of ABR waveform is used to diagnose conductive hearing loss or sensorineural hearing loss. In our previous study, we proved that the optimal decomposition level of the fast ABR is the level of D5 (frequency band is from 781 Hz to 1562 Hz), and explained that we used the reconstructed waveform of the ABR by inverse DWT (IDWT). Further more we proposed automated detection of the peak latency of wave V near-threshold according to input stimulus intensities using the template of normal I-L range. In this paper, we examine the following two points. Firstly, we apply the MRA to recorded ABR waveforms that are different number of averagings and we observe the reconstructed waveform at each resolution level. Secondly, we apply the MRA to recorded ABR waveforms that are obtained at the different sound stimulus intensity and we observe the reconstructed waveform at each resolution level.

A phase synchronization model between auditory brainstem response and electroencephalogram using the reconstructed waveform of multi-resolution discrete stationary wavelet analysis

The relationship between the slow component of auditory brainstem response (ABR) and the number of averaging is investigated using the multi-resolution discrete stationary wavelet analysis. A new model to analyze the phase shifts of the spontaneous electroencephalogram (EEG) is presented.

Waveform analysis of 40-Hz auditory steady-state response using wavelet analysis

A new design based on wavelet analysis for the objective audiometry devices is proposed. The auditory brainstem response and 80-Hz auditory steady-state response (ASSR) are used in the objective audiometry devices for infants. For the aged, an objective audiometry device is used in anti-aging investigations, which enables the hearing acuity of awake adults to be tested with the 40-Hz ASSR. The ASSR evoked by an amplitude modulated tone is recorded as a waveform. However, the evoked potential response is very small. Therefore, it is difficult to decide a threshold of the response and whether a significant response exists when it is mixed with noise such as the background brain waves. To cope with this problem, we need to average the evoked response waveforms. In particular, the 40-Hz ASSR has a large amount of noise caused by the background brain waves in comparison with the 80-Hz ASSR. In this paper, we apply waveform analysis using the wavelet transform in order to extract the 40-Hz ASSR from a signal mixed with a large amount of noise. Subjects with normal hearing participated in this study.

An application of wavelet analysis to procedure of averaging waveform of 40-Hz auditory steady-state response

The auditory steady-state response (ASSR) is one of auditory evoked brain responses applied to objective audiometry test. ASSR evoked by an amplitude-modulated tone is recorded as a waveform with the same frequency as the stimulus modulation frequency. Since the 40-Hz ASSR can be measured when subjects are awake, a rapid objective audiometry test has been desired for the 40-Hz ASSR. In the previous paper, based on wavelet analysis, we proposed a design of procedure of averaging waveform of 40-Hz ASSR for our original objective audiometry device. In this paper, we present detail examination using complex continuous wavelet analysis of characteristics of brain waves obtained by our original objective audiometry device. We also propose a Meyer type band-pass filter to extract the waveform data of around 40 Hz. The effectiveness of band pass filter is shown by an experiment.

A Model of Relationship Between Waveform-Averaging and Slow Auditory Brainstem Response by Using Discrete Stationary Wavelet Analysis

The relationship between the slow component of auditory brainstem response (ABR) and the number of averaging is investigated using the discrete stationary wavelet analysis (SWT). A new model to analyze the phase shifts of the spontaneous electroencephalogram (EEG) is presented.

Optimum wavelet filter estimating peak latencies of auditory brainstem response waveform

The peak latencies of auditory brainstem response (ABR) are useful to support of human auditory brain functional diagnosis. For example is useful to estimate audiogram in the human hearing test. In the previous study we proposed a method of analysis of the peak latencies of the ABR using stationary wavelet transform (SWT). Furthermore, in this paper we observe the ABR peak latencies near around human auditory threshold in which depicts in the audiogram by using SWT. At the same time we discuss about the optimum wavelet function to analyze the waveform of the human audiometry threshold depicted in the audiogram by using SWT.