Non-traditional method applied in ECG analysis using embedded collecting system
碩士 === 元智大學 === 機械工程學系 === 93 === Complex physiologic signals may carry unique dynamic signatures that are related to their underlying mechanisms. Based on non-traditional methods, such as detrended fluctuation analysis (DFA) and rank order statistics (ROS) of symbolic sequences, and traditional met...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2005
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| Online Access: | http://ndltd.ncl.edu.tw/handle/32056050199388251425 |
| Summary: | 碩士 === 元智大學 === 機械工程學系 === 93 === Complex physiologic signals may carry unique dynamic signatures that are related to their underlying mechanisms. Based on non-traditional methods, such as detrended fluctuation analysis (DFA) and rank order statistics (ROS) of symbolic sequences, and traditional method, such as power spectral analysis, we applied these methods to heart rate variability (HRV) in intensive care units (ICU) in order to determine which indexes are more accurate to help doctors diagnose patients in an ICU more rapidly in the future. Thirty three patients with 27 light cases and 6 serious cases of acute myocardial infarction (AMI) patients at hospital in ICU were studied as group A. This group was collected electrocardiograph (ECG) signals lasting around 60 min using an industrial personal computer (IPC). Ten college student volunteers as group B for comparison with group A also provided ECG signals lasting around 60 min using PIC microprocessor technology. It was found that DFA can clearly distinguish pathologic states of AMI patients in ICU in comparison with the healthy group. However, the ROS and power spectral analysis are more sensitive to the status of either AMI patients or volunteers.
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