Evaluating the Audiological Testing Process Through Galvanic Skin Response Using a One-Dimensional Convolutional Neural Network
The evaluation of audiometric tests, which assess an individual's ability to perceive various sounds and frequencies, is crucial for diagnosing and monitoring hearing loss. This study aims to evaluate the effects of the audiological testing process on individuals by classifying their galvanic s...
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| Veröffentlicht in: | Traitement du signal 2024-08, Vol.41 (4), p.2153-2158 |
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| creator | Polat, Lütfiye Nurel Özdinç Özen, Şükrü |
| description | The evaluation of audiometric tests, which assess an individual's ability to perceive various sounds and frequencies, is crucial for diagnosing and monitoring hearing loss. This study aims to evaluate the effects of the audiological testing process on individuals by classifying their galvanic skin response (GSR) with a one-dimensional convolutional neural network (1D-CNN). GSR, which reflects physiological changes due to psychological states such as stress and relaxation, was measured during audiological tests to distinguish between resting and active states. Various transformations of the GSR data were applied to the 1D-CNN input to determine the most effective method in classification. The results demonstrate that GSR data, when processed through 1D-CNN, can reliably reflect the physiological and emotional impacts of audiological testing on individuals. This approach provides a novel method for enhancing the understanding of the audiological test experience through objective physiological measures. |
| doi_str_mv | 10.18280/ts.410443 |
| format | Article |
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| subjects | Accuracy Algorithms Artificial neural networks Classification Data compression Deep learning Electroencephalography Emotions Evaluation Fourier transforms Galvanic skin response Hearing loss Machine learning Neural networks Physiological effects Signal processing Stress relaxation Success Volunteers Women |
| title | Evaluating the Audiological Testing Process Through Galvanic Skin Response Using a One-Dimensional Convolutional Neural Network |
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