Development and Validation of Deep Neural Networks for Blinking Identification and Classification

Study Overview
=================
Brief Summary
-----------------
Primary objective of this study is the development and validation of a system of deep neural networks which automatically detects and classifies blinks as complete or incomplete in image sequences.

Detailed Description
-----------------
This method is based on iris and sclera segmentation in both eyes from the acquired images, using state of the art deep learning encoder-decoder neural architectures (DLED). The sequence of the segmented frames is post-processed to calculate the distance between the eyelids of each eye (palpebral fissure) and the corresponding iris diameter. Theses quantities are temporally filtered and their fraction is subject to adaptive thresholding to identify blinks and determine their type, independently for each eye. The two DLEDs were trained with manually segmented images and the post-process was parameterized using a 4-minute video. After DLED training, the proposed system was tested on 8 different subjects, each one with a 4-10-minute video. Several metrics of blink detection and classification accuracy were calculated against the ground truth, which was generated by 3 independent experts, whose conflicts were resolved by a senior expert. Two independent blink identifications are assumed to be in agreement, if and only if there is sufficient temporal overlapping and the type of blink is the same between the DLED system and the ground truth.

Official Title
-----------------
Development and Validation of Deep Neural Networks for Blinking Identification and Classification

Conditions
-----------------
Blinking, Deep Learning

Intervention / Treatment
-----------------
* Diagnostic Test: Comparison of the proposed artificial network with the ground truth


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria Uncorrected Distance Visual Acuity above 6/12 Exclusion Criteria: corneal opacities age-related macular degeneration diagnosis of psychiatric diseases former eyelid surgery

Ages Eligible for Study
-----------------
Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Study group<br>8 patients aged between 18 to 75 years with Uncorrected Distance Visual Acuity ≥ 5/10 | Diagnostic Test: Comparison of the proposed artificial network with the ground truth<br>* Both eyes will be included for each study participant. Participants watched a 4-10-minute video in standard mesopic environmental lighting conditions at 3.5m viewing distance. Simultaneously, all blinking moves will be recorded through a web infrared camera. The proposed system was tested on the 8 different subjects. Several metrics of blink detection and classification accuracy were calculated against the ground truth, which was generated by 3 independent experts, whose conflicts were resolved by a senior expert. Two independent blink identifications are assumed to be in agreement, if and only if there is sufficient temporal overlapping and the type of blink is the same between the DLED system and the ground truth.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of complete and incomplete blinks | Complete and incomplete blinks are defined by the length of palpebral fissure-to-iris diameter ratio | up to 1 week | 
| First frame of each blink | The frame in which the upper eyelid starts to move down and cover the cornea | up to 1 week | 
| Last frame of each blink | The frame in which eyelids open fully after a blink | up to 1 week | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Length of palpebral fissure of both eyes | The distance between the upper eyelid margin and the lower eyelid margin (ie. the vertical dimension of the palpebral fissure), | up to 1 week | 
| Iris diameter of both eyes | The horizontal diameter of the iris (ie. the horizontal white-to white distance) | up to 1 week | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
deep learning, DeepLabv3+, iris and sclera segmentation, eye blink detection, complete and incomplete blink classification