Abstract
Continuous automatic heart rate (HR) monitoring plays a crucial role in timely intervention for postoperative patients. However, for effective alarm management, patients’ activities of daily living need to be considered as they influence HR. This explorative study aimed to develop a heartrate prediction system while performing six activities. An experiment with fourteen participants was conducted to gather data to build a system. This system consisted of a support-vector machine classifier for activity recognition and a k-Nearest Neighbors regressor for HR prediction.
The R-squared (a goodness-of-fit measure) of the HR predictor is 79% on average. Given the heterogeneity of different populations, the system will be further tested and developed using patient datasets in future towards linical practice applications.
The R-squared (a goodness-of-fit measure) of the HR predictor is 79% on average. Given the heterogeneity of different populations, the system will be further tested and developed using patient datasets in future towards linical practice applications.
Original language | English |
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Number of pages | 4 |
DOIs | |
Publication status | Published - 13 Oct 2022 |
Event | 49th Computing in Cardiology Conference, CinC 2022 - Tampere, Finland Duration: 4 Sept 2022 → 7 Sept 2022 Conference number: 49 https://cinc.org/ |
Conference
Conference | 49th Computing in Cardiology Conference, CinC 2022 |
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Abbreviated title | CinC 2022 |
Country/Territory | Finland |
City | Tampere |
Period | 4/09/22 → 7/09/22 |
Internet address |
Keywords
- Vital signs
- heart rate monitoring
- Human Activity Recognition
- heart rate prediction
- Accelerometer