TY - GEN
T1 - SleepSentry
T2 - 2025 IEEE International Conference on Consumer Electronics, ICCE 2025
AU - Saha, Pranay
AU - Sayeed, Md Abu
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Sleep apnea (SA) is a sleep disorder that affects 5 to 10 percent of the world's population and about 39 million adults in the United States. It disrupts sleep either by partial stoppage or complete blockage of breathing and leads to serious health conditions if left untreated. Current diagnostic solutions for sleep apnea are costly and time-consuming. In this work, a convolutional neural network (CNN) based fast and automated sleep apnea detection system has been presented which provides comprehensive solutions for patient healthcare. The use of CNN after filtering the test data makes the system highly optimized and fast. The integration of Internet of Things (IoT) technology enables data to be securely pushed to servers for further analysis and user notifications. The publicly accessible PhysioNet dataset is used for validation, and the proposed method improves upon existing solutions, making it a strong contender for application in wearable medical devices.
AB - Sleep apnea (SA) is a sleep disorder that affects 5 to 10 percent of the world's population and about 39 million adults in the United States. It disrupts sleep either by partial stoppage or complete blockage of breathing and leads to serious health conditions if left untreated. Current diagnostic solutions for sleep apnea are costly and time-consuming. In this work, a convolutional neural network (CNN) based fast and automated sleep apnea detection system has been presented which provides comprehensive solutions for patient healthcare. The use of CNN after filtering the test data makes the system highly optimized and fast. The integration of Internet of Things (IoT) technology enables data to be securely pushed to servers for further analysis and user notifications. The publicly accessible PhysioNet dataset is used for validation, and the proposed method improves upon existing solutions, making it a strong contender for application in wearable medical devices.
KW - Blood Oxygen Saturation(Spo2)
KW - Convolutional Neural Network(CNN)
KW - Electrocardiogram(ECG)
KW - Internet of Things (IoT)
KW - Sleep Apnea (SA)
UR - http://www.scopus.com/inward/record.url?scp=105006643144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105006643144&partnerID=8YFLogxK
U2 - 10.1109/ICCE63647.2025.10930207
DO - 10.1109/ICCE63647.2025.10930207
M3 - Conference contribution
AN - SCOPUS:105006643144
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2025 IEEE International Conference on Consumer Electronics, ICCE 2025
Y2 - 11 January 2025 through 14 January 2025
ER -