TY - GEN
T1 - Sleep Apnea Classification Using KNN
AU - Kamath, Surekha
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Sleep is a state of reduced mental and physical activity, in which consciousness is altered. And all the sensory activity is stopped to certain extent. Maintenance of good sleep is very essential for each individual. Nowadays, diseases related to sleep are very common among the population due to individual living styles and stress related disorder. One such sleep disorder among them is obstructive sleep apnea (OSA), in which an individual experiences difficulty in breathing. Delayed or improper detection of this disorder can cause serious health issues. This work explains the classification of sleep apnea using K-nearest neighbor algorithm using various features. The proposed work is to extract the statistical features such as mean, median, standard deviation and mean square error of ECG signal to detect the abnormal pauses in breathing during sleep apnea period. Classification of sleep stages can be performed using K-nearest neighbor classifier. The KNN classifier was found to work with an accuracy of 95%.
AB - Sleep is a state of reduced mental and physical activity, in which consciousness is altered. And all the sensory activity is stopped to certain extent. Maintenance of good sleep is very essential for each individual. Nowadays, diseases related to sleep are very common among the population due to individual living styles and stress related disorder. One such sleep disorder among them is obstructive sleep apnea (OSA), in which an individual experiences difficulty in breathing. Delayed or improper detection of this disorder can cause serious health issues. This work explains the classification of sleep apnea using K-nearest neighbor algorithm using various features. The proposed work is to extract the statistical features such as mean, median, standard deviation and mean square error of ECG signal to detect the abnormal pauses in breathing during sleep apnea period. Classification of sleep stages can be performed using K-nearest neighbor classifier. The KNN classifier was found to work with an accuracy of 95%.
UR - https://www.scopus.com/pages/publications/85205361810
UR - https://www.scopus.com/pages/publications/85205361810#tab=citedBy
U2 - 10.1007/978-981-97-5412-0_1
DO - 10.1007/978-981-97-5412-0_1
M3 - Conference contribution
AN - SCOPUS:85205361810
SN - 9789819754113
T3 - Lecture Notes in Networks and Systems
SP - 1
EP - 12
BT - Intelligent Computing Systems and Applications - Proceedings of the 2nd International Conference, ICICSA 2023
A2 - Bandyopadhyay, Sivaji
A2 - Balas, Valentina Emilia
A2 - Biswas, Saroj Kumar
A2 - Saha, Anish Kumar
A2 - Thounaojam, Dalton Meitei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023
Y2 - 21 September 2023 through 22 September 2023
ER -