scholarly journals A Method for Modeling and Feature Extraction of Auditory Brainstem Responses Using Kalman Filter

2004 ◽  
Vol 8 (4) ◽  
pp. 335-349 ◽  
Author(s):  
Nobuko Ikawa ◽  
Takashi Yahagi
2016 ◽  
Vol 43 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Amir Salar Jafarpisheh ◽  
Amir Homayoun Jafari ◽  
Mohammadjavad Abolhassani ◽  
Mohammad Farhadi ◽  
Hamed Sadjedi ◽  
...  

2019 ◽  
Vol 28 (1) ◽  
pp. 114-124
Author(s):  
Linda W. Norrix ◽  
Julie Thein ◽  
David Velenovsky

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.


2020 ◽  
Vol 29 (3) ◽  
pp. 391-403
Author(s):  
Dania Rishiq ◽  
Ashley Harkrider ◽  
Cary Springer ◽  
Mark Hedrick

Purpose The main purpose of this study was to evaluate aging effects on the predominantly subcortical (brainstem) encoding of the second-formant frequency transition, an essential acoustic cue for perceiving place of articulation. Method Synthetic consonant–vowel syllables varying in second-formant onset frequency (i.e., /ba/, /da/, and /ga/ stimuli) were used to elicit speech-evoked auditory brainstem responses (speech-ABRs) in 16 young adults ( M age = 21 years) and 11 older adults ( M age = 59 years). Repeated-measures mixed-model analyses of variance were performed on the latencies and amplitudes of the speech-ABR peaks. Fixed factors were phoneme (repeated measures on three levels: /b/ vs. /d/ vs. /g/) and age (two levels: young vs. older). Results Speech-ABR differences were observed between the two groups (young vs. older adults). Specifically, older listeners showed generalized amplitude reductions for onset and major peaks. Significant Phoneme × Group interactions were not observed. Conclusions Results showed aging effects in speech-ABR amplitudes that may reflect diminished subcortical encoding of consonants in older listeners. These aging effects were not phoneme dependent as observed using the statistical methods of this study.


2020 ◽  
Vol 63 (11) ◽  
pp. 3877-3892
Author(s):  
Ashley Parker ◽  
Candace Slack ◽  
Erika Skoe

Purpose Miniaturization of digital technologies has created new opportunities for remote health care and neuroscientific fieldwork. The current study assesses comparisons between in-home auditory brainstem response (ABR) recordings and recordings obtained in a traditional lab setting. Method Click-evoked and speech-evoked ABRs were recorded in 12 normal-hearing, young adult participants over three test sessions in (a) a shielded sound booth within a research lab, (b) a simulated home environment, and (c) the research lab once more. The same single-family house was used for all home testing. Results Analyses of ABR latencies, a common clinical metric, showed high repeatability between the home and lab environments across both the click-evoked and speech-evoked ABRs. Like ABR latencies, response consistency and signal-to-noise ratio (SNR) were robust both in the lab and in the home and did not show significant differences between locations, although variability between the home and lab was higher than latencies, with two participants influencing this lower repeatability between locations. Response consistency and SNR also patterned together, with a trend for higher SNRs to pair with more consistent responses in both the home and lab environments. Conclusions Our findings demonstrate the feasibility of obtaining high-quality ABR recordings within a simulated home environment that closely approximate those recorded in a more traditional recording environment. This line of work may open doors to greater accessibility to underserved clinical and research populations.


Skull Base ◽  
2007 ◽  
Vol 16 (S 2) ◽  
Author(s):  
Magdalini Tsiakou ◽  
Thomas Nikolopoulos ◽  
Olga Papadopoulou ◽  
Maria Theodosiou ◽  
Sotirios Pappas ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.


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