Signal quality and data fusion for false alarm reduction in the intensive care unit.
Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological feat...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2012
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| _version_ | 1797099252366704640 |
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| author | Li, Q Clifford, G |
| author_facet | Li, Q Clifford, G |
| author_sort | Li, Q |
| collection | OXFORD |
| description | Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required. |
| first_indexed | 2024-03-07T05:21:07Z |
| format | Journal article |
| id | oxford-uuid:def4a53c-bfc4-4aa5-bd3a-3263f5cbb572 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:21:07Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:def4a53c-bfc4-4aa5-bd3a-3263f5cbb5722022-03-27T09:35:44ZSignal quality and data fusion for false alarm reduction in the intensive care unit.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:def4a53c-bfc4-4aa5-bd3a-3263f5cbb572EnglishSymplectic Elements at Oxford2012Li, QClifford, GDue to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required. |
| spellingShingle | Li, Q Clifford, G Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title | Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title_full | Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title_fullStr | Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title_full_unstemmed | Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title_short | Signal quality and data fusion for false alarm reduction in the intensive care unit. |
| title_sort | signal quality and data fusion for false alarm reduction in the intensive care unit |
| work_keys_str_mv | AT liq signalqualityanddatafusionforfalsealarmreductionintheintensivecareunit AT cliffordg signalqualityanddatafusionforfalsealarmreductionintheintensivecareunit |