Hearing screening through frequency analysis of auditory brainstem response using PhysioNet Data
https://doi.org/10.47895/amp.v57i9.4336
- Author:
Catherine Manuela Lee-Ramos
1
;
Al Francis L. Bontogon
2
;
Angelica S. Collanto
2
;
Patrick John P. Labra
3
;
Luis G. Sison
4
;
Charlotte M. Chiong
3
Author Information
1. Manufacturing Engineering and Management, De La Salle University, Manila, Philippines
2. Electronics, Communications and Computer Engineering Department, Ateneo de Manila University, Quezon City, Philippines
3. Philippine National Ear Institute, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
4. Electrical and Electronics Engineering Institute, University of the Philippines Diliman, Quezon City, Philippines
- Publication Type:Journal Article
- Keywords:
Auditory;
Hearing loss humans
- MeSH:
Evoked Potentials;
Brain Stem;
Delayed Diagnosis
- From:
Acta Medica Philippina
2023;57(9):32-38
- CountryPhilippines
- Language:English
-
Abstract:
Objectives:Responding to the reality of neonate patients with delayed childhood development due to late diagnosis of and intervention on hearing impairment, this study aims to determine the features based on time-frequency domain of auditory brainstem response (ABR) signals and to test the protocol on ABR signals from PhysioNet.
Methods:This is done by pre-processing, performing time-frequency analysis, and characterizing hearing impairment using the dominant features of the ABR. In this study, normal (N) and hearing impaired (HI) ABR adult human signals were acquired from Physionet.org, a publicly available database. Considering its high signal-to-noise ratio, numerous filters and transformations were applied to extract the ABR. Consequently, the features acquired — dominant frequency and bigrams, were used as data classifiers.
Results:Initial results using only N classifiers, that is features from the Normal dataset, and bandpass Chebyshev filter with a lower cut-off frequency of 60 Hz show that the tests yielded low to middle sensitivity. Further tests were done to improve the sensitivity that incorporated the HI classifiers, used data filtered with a low cut-off frequency of 300 Hz, and data divided per stimulus intensity level.
Conclusion:Conclusions made are 1) data with both N and HI classifiers have higher sensitivity than those using only N classifiers, 2) data with a Chebyshev cut-off frequency of 300 Hz have a higher sensitivity than those with 60 Hz, and 3) data divided per intensity level have a higher sensitivity than data analyzed as a whole, and that features with stimulus intensity in middle ranges have a better distinction between HI and N patients.
- Full text:4336-Article Text-125650-1-10-20230928.pdf
