Name
Abstract | ||
|---|---|---|
Objective: The aim was to propose a cuff-less, cost-efficient, and ultra-convenient blood pressure monitoring technique with a 3-axis accelerometer. Methods: The efficacy of the proposed approach was examined in 8 young healthy volunteers undergoing different activities with a 3-axis accelerometer leveled on their upper chest. The 3-dimensional accelerations were exploited to select features for the calculation of systolic pressure (SP) and diastolic pressure (DP); the whole process involved signal processing, feature extraction, linear multivariate regression, and leave-one-out cross validations (LOOCV). Results: DP and SP could be approximated with the linear combination of the extracted features: the L-2 norm of lateral acceleration for both DP and SP, state variation (defined in the proposed algorithm) of vertical acceleration for SP, and I-J interval (defined in ballistocardiogram) of vertical acceleration for DP. The correlation coefficient (r) of the estimated and the measured DP was 0.97, and for SP, r = 0.96. In LOOCV, our best validated results in difference errors were -0.02 +/- 3.82 mmHg for DP and -0.59 +/- 7.46 mmHg for SP. Conclusion: Compared to AAMI criteria, the proposed acceleration-based technique fulfilled the requirement. The accelerometer-based technique showed the potential to monitor blood pressure cufflessly, cost-efficiently, ultra-conveniently, and to be embedded in a long-term wearable device for clinical usage. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857864 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Biomedical engineering,Computer vision,Signal processing,Linear combination,Correlation coefficient,Computer science,Accelerometer,Multivariate statistics,Feature extraction,Acceleration,Artificial intelligence,Blood pressure | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Chang | 1 | 0 | 0.34 |
| Chung-Kuan Cheng | 2 | 2314 | 285.85 |
| Anushka Gupta | 3 | 0 | 0.34 |
| Po-Han Hsu | 4 | 1 | 1.69 |
| Po-Ya Hsu | 5 | 5 | 3.47 |
| Hsin-Li Liu | 6 | 0 | 0.34 |
| Amanda Moffitt | 7 | 0 | 0.34 |
| Alissa Ren | 8 | 0 | 0.34 |
| Irene Tsaur | 9 | 0 | 0.34 |
| Samuel Wang | 10 | 0 | 0.34 |