Area asymmetry of heart rate variability signal
Abstract 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 o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-09-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | http://link.springer.com/article/10.1186/s12938-017-0402-3 |
| _version_ | 1811194645178220544 |
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| author | Chang Yan Peng Li Lizhen Ji Lianke Yao Chandan Karmakar Changchun Liu |
| author_facet | Chang Yan Peng Li Lizhen Ji Lianke Yao Chandan Karmakar Changchun Liu |
| author_sort | Chang Yan |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T00:30:14Z |
| format | Article |
| id | doaj.art-c100212659e7459297dd0483cf7ae43e |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-04-12T00:30:14Z |
| publishDate | 2017-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-c100212659e7459297dd0483cf7ae43e2022-12-22T03:55:22ZengBMCBioMedical Engineering OnLine1475-925X2017-09-0116111410.1186/s12938-017-0402-3Area asymmetry of heart rate variability signalChang Yan0Peng Li1Lizhen Ji2Lianke Yao3Chandan Karmakar4Changchun Liu5School of Control Science and Engineering, Shandong UniversitySchool of Control Science and Engineering, Shandong UniversitySchool of Control Science and Engineering, Shandong UniversitySchool of Control Science and Engineering, Shandong UniversitySchool of Information Technology, Deakin UniversitySchool of Control Science and Engineering, Shandong UniversityAbstract 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.http://link.springer.com/article/10.1186/s12938-017-0402-3Heart rate asymmetry (HRA)Heart rate variability (HRV)Poincaré plotArea asymmetryPhase asymmetry |
| spellingShingle | Chang Yan Peng Li Lizhen Ji Lianke Yao Chandan Karmakar Changchun Liu Area asymmetry of heart rate variability signal BioMedical Engineering OnLine Heart rate asymmetry (HRA) Heart rate variability (HRV) Poincaré plot Area asymmetry Phase asymmetry |
| title | Area asymmetry of heart rate variability signal |
| title_full | Area asymmetry of heart rate variability signal |
| title_fullStr | Area asymmetry of heart rate variability signal |
| title_full_unstemmed | Area asymmetry of heart rate variability signal |
| title_short | Area asymmetry of heart rate variability signal |
| title_sort | area asymmetry of heart rate variability signal |
| topic | Heart rate asymmetry (HRA) Heart rate variability (HRV) Poincaré plot Area asymmetry Phase asymmetry |
| url | http://link.springer.com/article/10.1186/s12938-017-0402-3 |
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