TY - GEN
T1 - Automatic detection of sleep arousal events from polysomnographic biosignals
AU - Shahrbabaki, Sobhan Salari
AU - Dissanayaka, Chamila
AU - Patti, Chanakya Reddy
AU - Cvetkovic, Dean
PY - 2015/12/7
Y1 - 2015/12/7
N2 - Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. K-nearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics.
AB - Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. K-nearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics.
UR - http://www.scopus.com/inward/record.url?scp=84962767333&partnerID=8YFLogxK
U2 - 10.1109/BioCAS.2015.7348363
DO - 10.1109/BioCAS.2015.7348363
M3 - Conference contribution
AN - SCOPUS:84962767333
T3 - IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
BT - IEEE Biomedical Circuits and Systems Conference
PB - Institute of Electrical and Electronics Engineers
T2 - 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Y2 - 22 October 2015 through 24 October 2015
ER -