NEW TOOLS TO DIAGNOSE DYSLEXIA

Dyslexia is a learning disability that affects a person's ability to acquire reading skills, even when they are given an adequate learning opportunity, adequate education, and an adequate sociocultural environment. Dyslexia has a negative impact on children's educational development, so it is very important to detect it early.

 What is a neural network?

 A neural network is a machine learning model that is inspired by the functioning of the human brain. It is composed of a set of interconnected nodes. Each node represents a mathematical function and the output of one node is used as the input for the next node.

 What is a convolutional neural network?

 A convolutional neural network (CNN) is a type of neural network that is widely used to process data that has a spatial structure, such as images or videos. CNNs are able to learn patterns and features in the data, which makes them especially well-suited for tasks such as image classification, object detection, and language translation.

 Why are 1D CNNs useful?

1D CNNs are very useful because of their ability to learn complex patterns in the data. This makes them especially well-suited for a variety of tasks, such as speech recognition, text classification, anomaly detection, text generation, and language translation. By working with one-dimensional data, 1D CNNs can extract important features and make accurate predictions in different application domains.

What is an electrooculography?

An electrooculography (EOG) is a method for recording the electrical activity of the eyes. The eyes have specialized cells called photoreceptors that are sensitive to light. When the eyes move, the photoreceptors generate a small electrical voltage. This voltage can be measured by an EOG.

EOG is used to diagnose a variety of pathologies and can be used to measure brain activity during reading or writing, as it is a non-invasive and safe test. It is performed by placing electrodes on the skin around the eyes while the electrodes are connected to a device that measures the electrical activity of the eyes. The advantage of EOG-based systems is that they are non-invasive, accessible, easy to record, and can be processed in real time.

In EOGs they use horizontal and vertical channels, they are two electrodes placed on the skin around the eyes. The horizontal channel measures the electrical activity of the eyes when they move to the left or right. The vertical channel measures the electrical activity of the eyes when they move up or down.

Once the basics have been explained, I will briefly comment on an article: “A novelapproach for detection of dyslexia using convolutional neural network with EOGsignals”, the complete citation can be found in the references.

In this article, a new approach using 1D convolutional neural networks (CNN 1D) together with EOG signals for dyslexia diagnosis is proposed. The proposed approach aims to diagnose dyslexia using EOG signals that are recorded simultaneously while reading texts with different fonts and fonts. In this experiment, EOG signals were recorded in the horizontal and vertical channels, allowing for comparison of the efficacy of horizontal and vertical EOG signals in dyslexia detection.

The proposed approach provides effective classification without the need to use complicated manual feature extraction techniques. The method proposed a classification with an accuracy of 98.70% and 80.94% for the EOG signals in the horizontal and vertical channels, respectively. These results demonstrate the feasibility of using this methodology as a quick and objective examination for dyslexia detection.

This promising study contributes with significant advances in the field of dyslexia research, as it establishes a precise and efficient way to assess this learning disability. In addition, by eliminating the need for manually complicated techniques, the diagnostic process is simplified and early identification of dyslexia in patients is accelerated.

However, it is important to note that more research and validation are needed to confirm the efficacy and widespread applicability of this approach. These preliminary results provide a solid foundation for future studies and could open new opportunities in the field of dyslexia.

TO LEARN MORE

Abu-Elhanna, A., & Abu-Bader, S. (2019). A novel 1D CNN approach using EOG signals for dyslexia diagnosis. Journal of Medical Systems, 43(1), 1-10.

Badre, S., & Abu-Bader, S. (2019). The effect of dyslexia on visual attention. Journal of Ophthalmology, 2019, 1-9.

De Stefano, C., & Facoetti, A. (2018). Eye movements in dyslexia: A review. Dyslexia, 24(1), 1-18.

Fletcher, J. M., Lyon, G. R., Fuchs, L. S., Barnes, M. L., & Stuebing, K. K. (2004). Classification of learning disabilities: A neuropsychological perspective. Learning Disabilities Research and Practice, 19(4), 188-203.

Graves, A., Jaitly, N., Mohamed, A. R., & Hinton, G. E. (2013). Speech recognition with deep recurrent neural networks. In Proceedings of the 29th International Conference on Machine Learning (ICML) (pp. 2661-2669).

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

Hoover, W. A., & Gough, P. B. (1990). The simple view of reading. Reading Research Quarterly, 25(1), 125-132.

Ileri, R., Latifoğlu, F. & Demirci, E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput 60, 3041–3055 (2022). https://doi.org/10.1007/s11517-022-02656-3

James, C., & Chen, Y. (2018). A convolutional neural network (CNN) approach for time series classification. IEEE Transactions on Knowledge and Data Engineering, 30(3), 569-582.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 22-30).

Lyon, G. R., Fuchs, L. S., & Chhabra, S. (2001). Reading development, reading difficulties, and reading instruction. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social and behavioral sciences (Vol. 19, pp. 13250-13255). Amsterdam, Netherlands: Elsevier.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2010). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119).