TY - GEN
T1 - Pulse transit time and heart rate variability in sleep staging
AU - Shahrbabaki, Sobhan Salari
AU - Ahmed, Beena
AU - Penzel, Thomas
AU - Cvetkovic, Dean
PY - 2016/10/18
Y1 - 2016/10/18
N2 - This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.
AB - This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.
KW - Sleep
KW - Heart rate variability
KW - Electrocardiography
KW - Sensitivity
KW - Classification algorithms
KW - Algorithm design and analysis
KW - Feature extraction
UR - http://www.scopus.com/inward/record.url?scp=85009075227&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7591475
DO - 10.1109/EMBC.2016.7591475
M3 - Conference contribution
C2 - 28269047
AN - SCOPUS:85009075227
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3469
EP - 3472
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -