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Published on: June 5, 2019
Area asymmetry of heart rate variability signal
Chang Yan1, Peng Li1, Lizhen Ji1
1School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
Insights
A new area index (AI) robustly assesses heart rate asymmetry (HRA) using short-term heartbeat data. This method shows promise for ambulatory cardiovascular monitoring, outperforming existing indices.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Heart rate asymmetry (HRA) describes beat-to-beat fluctuations.
- Assessing HRA from short heartbeat series is challenging.
Purpose of the Study:
- To develop and validate a new area index (AI) for robust HRA assessment.
- To compare AI performance against existing indices using short-term and long-term data.
- To evaluate AI's utility in classifying cardiac conditions like arrhythmia and heart failure.
Main Methods:
- Developed an area index (AI) using Poincaré plot features.
- Applied AI and existing indices (Porta's, Guzik's, slope index) to long-term and short-term ECG data.
- Classified subjects with arrhythmia and congestive heart failure against healthy controls.
Main Results:
- AI demonstrated superior performance on short-term data for classifying both arrhythmia and heart failure.
- AI showed better robustness and lower variability across short-term segments compared to other indices.
- Existing indices performed poorly on short-term data, with limited success for slope index on long-term data.
Conclusions:
- The proposed area index (AI) offers improved accuracy for HRA assessment, particularly with short-term data.
- AI's robustness suggests significant potential for ambulatory cardiovascular monitoring applications.
- This method could enhance the early detection and management of cardiac conditions.
Background:
Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series.
Method:
An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta's index (PI), Guzik's index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG.
Results:
For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p < 0.001, Cohen's d = 0.93, AUC (area under the receiver-operating characteristic curve) = 0.86; for task ii p < 0.001, d = 0.88, AUC = 0.75. AI performed well for task ii (p < 0.001, d = 1.0, AUC = 0.78); for task i, though the difference was statistically significant (p < 0.001, AUC = 0.76), the effect size was small (d = 0.11). PI and GI failed in both tasks (p > 0.05, d < 0.4, AUC < 0.7 for all). However, for short-term segments, AI indicated better distinguishability for both tasks, i.e., for task i, p < 0.001, d = 0.71, AUC = 0.71; for task ii, p < 0.001, d = 0.93, AUC = 0.74. The rest three measures all failed with small effect sizes and AUC values (d < 0.5, AUC < 0.7 for all) although the difference in SI for task i was statistically significant (p < 0.001). Besides, AI displayed smaller variations across different short-term segments, indicating more robust performance. Results from the second short-term analysis were in keeping with those findings.
Conclusion:
The proposed AI indicated better performance especially for short-term heartbeat interval data, suggesting potential in the ambulatory application of cardiovascular monitoring.
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