Design and Acquisition of EOG Based Interactive Communications for ALS Patients

Abstract

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease caused by loss of function of spinal cord and brain stem nerve cells. Loss of function in nerve cells leads to weakness and explosion (atrophy) in the muscles. The outcome of the weakness in the muscles needs the help of someone. Despite the limitations of the movement, studies on methods of increasing the daily quality of life ALS patients are continuing. Electrooculogram (EOG) signals were taken from an instrumentation amplifier with 48dB gain and 107dB CMMR ratio. A 16Hz Low Pass Filter and a 50Hz Notch Filter were used to increase the signal to noise ratio. EOG signals are digitized with 10bit resolution ADC and applied to ATmega328 microcontroller. So, the software of microcontroller determines the horizontal and vertical movements of the eye. In this way, the interactive PC software was controlled by the EOG signals. EOG-based interactive software which was developed using the C # programming language has provided to patients' daily requirements, social media accounts etc. The system has been tested on healthy subjects and it has been seen that people can control the software by eye movements. As a result, system will be useful not only for ALS patients, but also for permanent or partially bedridden patient (MS, Hemiplegia, etc.) groups. And also, while the quality of life in the patients' own habitats is being raised, they will also be able to benefit from health services within the scope of home health services.

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

“…The variation in the obtained signal shows the fatigue level of the ocular muscles. 43 The variation of the signal is due to affected retinal pigment epithelium in both type 1 and type 2 diabetes. Both type 1 and 2 diabetes results in an increase in glucose level.…”

Design and Implementation of Eog Signal Based Wireless System to Control Smart Devices

“…Another article written by Octavio Rivera, et al (2013) [2], used an algorithm that captures the EOG signal and converts this vital signal into a mechanical one [3], which helped study and design a motorized wheelchair with EOG control for disabled people. Nese Ozkan, et al (2017) [4], studied the design and acquisition of EOG based interactive communications for patients with ALS by applying the EOG signal to them. In the article written by Lawrence Y. Deng Chun Liang Hsu (2002) [5], an EOG-based Human-Computer interface system was developed.…”