Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals
Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM)....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485851/?tool=EBI |
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| author | Carlos A. Robles-Rubio Robert E. Kearney Gianluca Bertolizio Karen A. Brown Wajid Mumtaz |
| author_facet | Carlos A. Robles-Rubio Robert E. Kearney Gianluca Bertolizio Karen A. Brown Wajid Mumtaz |
| author_sort | Carlos A. Robles-Rubio |
| collection | DOAJ |
| description | Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4o and 100o. Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events. |
| first_indexed | 2024-04-12T08:51:09Z |
| format | Article |
| id | doaj.art-f75fc70c73ce46d3aaa29b37bbcb0373 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T08:51:09Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj.art-f75fc70c73ce46d3aaa29b37bbcb03732022-12-22T03:39:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01159Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signalsCarlos A. Robles-RubioRobert E. KearneyGianluca BertolizioKaren A. BrownWajid MumtazInfants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4o and 100o. Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485851/?tool=EBI |
| spellingShingle | Carlos A. Robles-Rubio Robert E. Kearney Gianluca Bertolizio Karen A. Brown Wajid Mumtaz Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals PLoS ONE |
| title | Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| title_full | Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| title_fullStr | Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| title_full_unstemmed | Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| title_short | Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| title_sort | automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485851/?tool=EBI |
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