ABSTRAT:
Obstructive Sleep Apnoea (OSA) occurs when obstruction happens repeatedly in the airway during sleep due to relaxation of the tongue and airway-muscles. Usual indicators of OSA are snoring, poor night sleep due to choking or gasping for air and waking up unrefreshed. OSA diagnosis is costly both in the monetary and timely manner. That is why many patients remain undiagnosed and unaware of their condition. Previous research has shown the link between facial morphology and OSA. In this paper, we investigate the application of deep learning techniques to diagnose the disease through depth map of human facial scans. Depth map will provide more information about facial morphology as compared to the plain 2-D colour image. Even with very less amount of sample data, we are able to get around 69validation accuracy using transfer learning. We are predicting patients with above moderate > 15 or below moderate
EXISTING SYSTEM :
the model usual practice is to keep the learning rate of learned layers much less than the layers we are adding. We tried different learning rates and also tried by freezing different layers in each network. We set the batch size of 10 and fine-tuned the models through stochastic gradient descent (SGD). We started from pre-learned weights on VGG face, PAMs-VGG19, and PAMs-Alex Net and initialize fc8 layer from scratch. The initial learning rate is set to 0.0001. We fine-tune all the layers with this learning rate but the new fc8 layer learning rate is kept 20 times higher than the original one. we separate 14 samples for Test Data and rest of 30% of data is separated for Validation and 70% is used for Training the models. Training is done on NVIDIA GeForce GTX 1080 using MATLAB Deep learning toolbox. We trained the network for end-to-end classification, where we will give the facial depth map of the patient and it will classify between sleep apnoea and non-sleep apnoea
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
These results show that features capturing the composite elements of craniofacial structures and regional adiposity can predict OSA better than demographic data (e.g. BMI or neck circumference) collected by clinical observations. However, digital photographs are two dimensional in nature and hence neither non-linear measurements nor measurements of the shape of craniofacial anatomy can be obtained. All of these approaches need manual landmark detection, which is time-consuming and also depend upon the experience of the person who will mark the key point on facial data. To our knowledge, there is only one work for sleep apnoea detection using automated landmark detection. AT Balaei, et al. [16] uses a regression approach to find 21 profile and 14 front face landmarks. An accuracy of 70% was achieved using trained classifier over detected landmarks. They also try to directly predict OSA form coloured facial data of 50×50 pixel from frontal and profile images through training of neural network. In that regard, they achieve an accuracy of 62 Three-dimensional surface imaging technologies have recently been developed that are well suited for imaging the human head, face and neck. Such imaging has been previously used to analyze craniofacial changes before and after various treatment modalities for OSA treatment [17], [18], however to date only one study has used this technique to obtain 3D surface images of the face from 40 OSA and 40 non-OSA subjects, analysing only the association of craniofacial obesity with the OSA severity. No comprehensive study has been undertaken with this technique to compare the discriminatory capacity of facial morphometry between individuals with and without OSA
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
