This document describes a project aimed at classifying Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) in patients using machine learning techniques. The project uses a dataset containing neurocognitive measures like EEG signal features and physiologic measures like heart rate and blood pressure. It aims to develop Python routines using QDA classification and RBF neural networks to analyze the data and accurately classify the severity of patients' OSAS, which current medical experts have difficulty doing by manually analyzing multiple data sources. The project is designed in modules and uses software tools like Python, PyQt, and SQLite3 for implementation.

1. Computer-Aided Detection of Obstructive Sleep
Apnea and Depression.
By
• D. Dayakar (19R11A0561)
• Mohd. Masliuddin (19R11A0577)
• Sai. Krishna (19R11A0582)
• Project Guide – G. Santhoshi

2. ABSTRACT
• Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) are
common conditions associated with poor quality of life. In this Project, we aim to classify
OSAS in patients, by executing machine learning techniques on a dataset comprising of
Neurocognitive Measures and Physiologic Measures. The neuro cognitive data comprises
of EEG signal features like Amplitude, Delta, Theta, Alpha and Beta Values.
• The physiologic data comprises of
heart_rate,skin_conductance,skin_temperature,cortisol_level,Systolic_BP and
Diastolic_BP. The In the existing system, all thoses data are analyzed by medical experts
in order to arrive at the severity of the OSAS patient. But, it is difficult for the experts to
correlate data from multiple sources, and arrive at a decision on severity. The proposed
system, aims at classifying the OSAS patients data set by using the Radial basis
function(RBF) neural networks classification. These results are tallied against the results
from QDA classification.

3. INTRODUCTION
• Obstructive Sleep Apnea Syndrome (OSAS)
and Major Depressive Disorder (MDD) are
common conditions associated with poor
quality of life.
• In this project we aim to classify OSAS
in patients, by executing machine learning
techniques on a dataset.
• Obstructive Sleep Apnea Syndrome (OSAS)
or Obstructive Sleep Apnea (OSA) is a
breathing disorder characterized by brief
obstruction of upper airways during sleep,
disrupting it.

4. PROBLEM STATEMENT
In today's world as there are limited
number of doctors to handle so many
customers. By using our project the
doctors can easily classify the patients
based on their symptoms.
Even though if Doctor's classify
the patients the accuracy may not be
perfect.
The Doctor's first need to understand
the patients reports and afterwards
they need to classify the patients.
By using our Computer-aided
Detection model the accuracy will be
perfect and it is time saving as
it produces the results very quickly.

5. OBJECTIVES OF OUR PROJECT
THE MAIN OBJECTIVE OF THIS PROJECT IS TO
CLASSIFY OBSTRUCTIVE SLEEP APNEA SYNDROME
(OSAS) AND MAJOR DEPRESSIVE DISORDER
(MDD) IN PATIENTS.
IN CURRENT EXISTING SYSTEM THE DOCTORS
WILL CLASSIFY THE PATIENT BY SEEING THE TEST
REPORTS.
THIS PROJECT ALSO CLASSIFIES IN WHICH
STAGE PATIENT IS SUFFERING BY GIVING
ACCURATE VALUES.

6. LITERATURE SURVEY
S/N Author's Name Features used Classifiers Signal & Dataset Performance
Se
(%)
Sp
(%)
Acc
(%)
1 M. Stroack Energy,
Variance
SVM, KNN,
ANN, LDA,
Naïve Bayes
EEG/ MIT-BIH
Polysomnography
97 97 97
2 D. Sey, S. Chaudhuri and S. Munshi Energy,
Entropy,
Variance
SVM, KNN EEG/ MIT-BIH
Database
100 98 99
3 U. Erdenebayar, Y. J. Kim and K. J. Lee Multiband
Entropy
KNN EEG/ St. Vincent
University Database
89 86 88
4 D. Padovano, A. Martinez – Rodrigo and J.
M. Pastor
Inter-band
Energy
KNN EEG/ St. Vincent
University Database
90 94 92
5 B. Nazli and V. H. Altural Subframes Fully Connected
Neural
Network
EEG signal/ St. Vincent
Database
77 78 76

7. MODULES
SPLIT UP
We have devided our project into 4 modules.
Module 1 : First module deals with the storing the OSAS
patients neuro cognitive data in the DB.
Module 2 : The second module deals with the storage of
physiologic data and creating a sub data set plot.
Module 3 & 4 : The third module deals with the development
of python routines to execute the QDA classification on the
OSAS Patinets data set and the final module deals with the
RBF neural net analysis of OSAS Patinets data.

8. HARDWARE
REQUIREMENTS
1. It requires a minimum of 2.16
GHz processor.
2. It requires a minimum of 4 GB
RAM.
3. It requires 64-bit architecture.
4. It requires a minimum storage
of 500GB.

9. SOFTWARE
REQUIREMENTS
1. It requires a 64-bit windows Operating
System.
2. Python Qt Designer for designing user
interface.
3. SQLite3 for storing database Entities.
4. Pyuic for converting the layout designed
user interface (UI) to python code.
5. Python language for coding.

10. SYSTEM
ARCHITECTURE

11. DESIGN
PHASE

12. DATA FLOW
DIAGRAM

13. USE CASE
DIAGRAM

14. ACTIVITY
DIAGRAM

15. SEQUENCE
DIAGRAM

16. APPLICATIONS OF OUR PROJECT
The project is useful to the Psycho Therapists to analyze the OSAS patients data from multiple sources.
The project is useful to the OSAS Patients as the severity of their disease is accurately classified.
The project is useful to the data analysts to get an understanding about the RBF Neural Network.
Technical advantages:
Using latest Python’s sklearn tool to implement the QDA and RBF Neural Net classifications.
Using Python, which is chosen as the best programming language, by the Programming Community.
More Functionality can be implemented with less no.of lines of code in Python.
PyQt tool is used to create the Graphical User interfaces.
All the Front end code is generated automatically by PyUIC.

17. CONCLUSION &
FUTURESCOPE
• CONCLUSION
This project entitled “Computer-Aided Detection of Obstructive
Sleep Apnea.” is useful to the Psycho Therapists to analyze the
OSAS patients data from multiple sources. The project is useful
to the OSAS Patients as the severity of their disease is accurately
classified. The project is useful to the data analysts to get an
understanding about the RBF Neural Network.
• FUTURE SCOPE
As of now, the project is implemented as a stand alone
application. The feasibility of expanding it as a web/mobile
application, needs to be further explored.

18. REFERRENCES
• M. R. Mannarino, F. Di Filippo, and M. Pirro, ‘‘Obstructive sleep apnea syndrome,’’ Eur. J. Int. Med., vol. 23, no.
7, pp. 586–593, Oct. 2012.
• M. Li, X. Li, and Y. Lu, ‘‘Obstructive sleep apnea syndrome and metabolic diseases,’’ Endocrinology, vol. 159, no.
7, pp. 2670–2675, Jul. 2018.
• A. H. Khandoker, M. Palaniswami, and C. K. Karmakar, ‘‘Support vector machines for automated recognition of
obstructive sleep apnea syndrome from ECG recordings,’’ IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 1, pp.
37–48, Jan. 2009.
• T. Young, M. Palta, J. Dempsey, J. Skatrud, S. Weber, and S. Badr, ‘‘The occurrence of sleep-disordered
breathing among middle-aged adults,’’ New England J. Med., vol. 328, pp. 1230–1235, Apr. 1993.

19. THANK YOU