6 research outputs found
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Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response
Abstract
Background: The analysis of the Auditory Brainstem Response (ABR) is of
fundamental importance to the investigation of the auditory system behaviour,
though its interpretation has a subjective nature because of the manual process
employed in its study and the clinical experience required for its analysis. When
analysing the ABR, clinicians are often interested in the identification of ABR signal
components referred to as Jewett waves. In particular, the detection and study of
the time when these waves occur (i.e., the wave latency) is a practical tool for the
diagnosis of disorders affecting the auditory system. Significant differences in
inter-examiner results may lead to completely distinct clinical interpretations of the
state of the auditory system. In this context, the aim of this research was to evaluate
the inter-examiner agreement and variability in the manual classification of ABR.
Methods: A total of 160 ABR data samples were collected, for four different stimulus
intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects
(5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the
manual classification of ABR components participated in the study. The Bland-Altman
statistical method was employed for the assessment of inter-examiner agreement
and variability. The mean, standard deviation and error for the bias, which is the
difference between examiners’ annotations, were estimated for each pair of
examiners. Scatter plots and histograms were employed for data visualization and
analysis.
Results: In most comparisons the differences between examiner’s annotations were
below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error
and standard deviation (>0.1 ms) that indicate the presence of outliers and thus,
discrepancies between examiners.
Conclusions: Our results quantify the inter-examiner agreement and variability of
the manual analysis of ABR data, and they also allows for the determination of
different patterns of manual ABR analysis
Objective auditory brainstem response classification using machine learning
The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening
The effect of internal through coolant on grinding performance on AISI1020 mildsteel
This paper presents the experimental result of grinding performance with an internal through coolant supply. Using this method, the coolant is delivered through the grinding wheel and this concept will allowed the coolant supplied directly to the contact zone. The experiment has been conducted on thin plate of AISI1020 mild steel, to ensure the effectiveness when using internal through coolant supply and the grinding result using internal through. The result of internal through coolant supply was compared with an external coolant supply. It was revealed that the temperature was lower and surface roughness was smaller when the proposed coolant supply concept used
Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis
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Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection
WOS: 000235480200010This paper presents a novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold. ABR is an important potential signal for determining objective audiograms. Its detection is usually performed by medical experts with often basic signal processing techniques. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients and a set of discrete wavelet transform (DWT) approximation coefficients are calculated and extracted from signals separately as three different sets of feature vectors. These features are then selected by a modified adaptive method, which mainly supports to the input dimension reduction via selecting the most significant feature components. In the second stage, the feature vectors are classified by a support vector machine (SVM) classifier which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability. After that the proposed system is applied to real ABR data and it is resulted in a very good sensitivity, specificity and accuracy levels for DCT coefficients such as 99.2%, 94.0% and 96.2%, respectively. Consequently, the proposed system can be used for recognition of ABRs for hearing threshold detection. (c) 2005 Elsevier Ltd. All rights reserved