A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation.
Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a h...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4752295?pdf=render |
| _version_ | 1819237935216590848 |
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| author | Ashwin Belle Sardar Ansari Maxwell Spadafore Victor A Convertino Kevin R Ward Harm Derksen Kayvan Najarian |
| author_facet | Ashwin Belle Sardar Ansari Maxwell Spadafore Victor A Convertino Kevin R Ward Harm Derksen Kayvan Najarian |
| author_sort | Ashwin Belle |
| collection | DOAJ |
| description | Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability. |
| first_indexed | 2024-12-23T13:28:14Z |
| format | Article |
| id | doaj.art-b4f1d83881034023aab27be5523705b9 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T13:28:14Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b4f1d83881034023aab27be5523705b92022-12-21T17:45:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01112e014854410.1371/journal.pone.0148544A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation.Ashwin BelleSardar AnsariMaxwell SpadaforeVictor A ConvertinoKevin R WardHarm DerksenKayvan NajarianAdvanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.http://europepmc.org/articles/PMC4752295?pdf=render |
| spellingShingle | Ashwin Belle Sardar Ansari Maxwell Spadafore Victor A Convertino Kevin R Ward Harm Derksen Kayvan Najarian A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. PLoS ONE |
| title | A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. |
| title_full | A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. |
| title_fullStr | A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. |
| title_full_unstemmed | A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. |
| title_short | A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. |
| title_sort | signal processing approach for detection of hemodynamic instability before decompensation |
| url | http://europepmc.org/articles/PMC4752295?pdf=render |
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