Name
Title | ||
|---|---|---|
Recognition Of Sleep/Wake States Analyzing Heart Rate, Breathing And Movement Signals |
Abstract | ||
|---|---|---|
This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen's kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857596 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Wake,Kappa,Data analysis,Pattern recognition,Computer science,Multinomial logistic regression,Lying,Artificial intelligence,Breathing,Heart rate,Sleep Stages | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maksym Gaiduk | 1 | 0 | 1.69 |
| Ralf Seepold | 2 | 69 | 23.11 |
| Thomas Penzel | 3 | 48 | 15.35 |
| Juan Antonio Ortega Redondo | 4 | 208 | 29.56 |
| Martin Glos | 5 | 17 | 4.76 |
| Natividad Martínez Madrid | 6 | 0 | 0.34 |