Automatic tracking of intra-beat intervals in cardiac signals based on an ECG-BCG model
Nowadays, measuring intra-beat intervals in cardiac signals, such as electrocardiogram (ECG) and ballistocardiogram (BCG), has become increasingly important. So it is necessary to develop some methods with both efficiency and accuracy. Most of existing methods are based on peak detection and the acc...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/73099 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Nowadays, measuring intra-beat intervals in cardiac signals, such as electrocardiogram (ECG) and ballistocardiogram (BCG), has become increasingly important. So it is necessary to develop some methods with both efficiency and accuracy. Most of existing methods are based on peak detection and the accuracy of these methods is determined by detected peaks. Manual detection is necessary to achieve high accuracy but peak detection performed by professionals could be expensive. In this dissertation, we propose a tracking-based method to automatically measure intra-beat intervals in cardiac signals. The method provides measurement and tracking of these intervals at the same time. More specifically, the method first models the relationship between a signal and interval as a linear system and then provides estimation of future intra-beat intervals by using Kalman filters. The method is tested using data obtained from four healthy subjects before and after exercise. From the results we obtained, the method achieves an average error below 5ms under pre-exercise condition which is accurate enough for most applications. Besides the performance analysis, we also provide discussions from signal processing viewpoint based on data and error in our experiment. |
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