Development of Improved Software Intelligent System for Audiological Solutions

Rajkumar, S.; Muttan, S.; Sapthagirivasan, V.; Jaya, V.; Vignesh, S. S.

doi:10.1007/s10916-018-0978-6

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…The study by Rajkumar S. and colleagues used artificial neural network methods to determine the hearing loss value and the appropriate hearing aid gain value. It was reported that 86% of individuals with hearing impairments were satisfied with the gain values recommended by the studies [10].…”

Section: Literature Reviewmentioning

confidence: 97%

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

et al. 2023

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This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.

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Section: Literature Reviewmentioning

confidence: 97%

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

et al. 2023

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“…Nonetheless, in (Or, et al, 2018) it is proposed that most of the modern and/or state-of-the-art health information systems still face challenges in terms of their output which is less efficient, and that is due to their under-explored techniques (or approaches) underlying the design of the systems (Nogueira, et al, 2018). In (Rajkumar, et al, 2018), there is a proposed improved software intelligent system towards enhancing the performance of a hearing impairment system in which the system is aimed to provide solution to audio logical problems. In (Singh, et al, 2017) is an introduced deep learning model towards recognition of human activity in which the model is able to learn to classify human activities without prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

Adu-Gyamfi¹,

Zhang

2021

Journal of Information Technology Research

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Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.

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“…Notwithstanding, Or et al [14] propose that most of the modern health information systems have under-explored design technique challenges [5]. Moreover, Rajkumar et al [15] propose an improved software intelligent system towards enhancing the performance of a hearing impairment system to provide a solution to audiological problems. Whereas, Singh et al [16] introduce a deep learning model towards human activity recognition such that without prior knowledge the model can be used to classify human activities.…”

Section: Introductionmentioning

confidence: 99%

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

Adu-Gyamfi

Zhang

Ansah

2020

BMC Med Inform Decis Mak

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Background A pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP. Methods In this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance. Results The EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic. Conclusions The EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic.

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Development of Improved Software Intelligent System for Audiological Solutions

Cited by 5 publications

References 26 publications

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

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