Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device
Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea−hypopnea events per hour of sleep (apnea−hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/286 |
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| author | Florent Baty Maximilian Boesch Sandra Widmer Simon Annaheim Piero Fontana Martin Camenzind René M. Rossi Otto D. Schoch Martin H. Brutsche |
| author_facet | Florent Baty Maximilian Boesch Sandra Widmer Simon Annaheim Piero Fontana Martin Camenzind René M. Rossi Otto D. Schoch Martin H. Brutsche |
| author_sort | Florent Baty |
| collection | DOAJ |
| description | Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea−hypopnea events per hour of sleep (apnea−hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7−40] <inline-formula> <math display="inline"> <semantics> <msup> <mi mathvariant="normal">h</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math> </inline-formula>. The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up. |
| first_indexed | 2024-04-11T22:23:32Z |
| format | Article |
| id | doaj.art-2cc1ce41d4854959a529384fdad38037 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:23:32Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2cc1ce41d4854959a529384fdad380372022-12-22T03:59:56ZengMDPI AGSensors1424-82202020-01-0120128610.3390/s20010286s20010286Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable DeviceFlorent Baty0Maximilian Boesch1Sandra Widmer2Simon Annaheim3Piero Fontana4Martin Camenzind5René M. Rossi6Otto D. Schoch7Martin H. Brutsche8Cantonal Hospital St. Gallen, Lung Center, Rorschacherstrasse 95, 9007 St. Gallen, SwitzerlandCantonal Hospital St. Gallen, Lung Center, Rorschacherstrasse 95, 9007 St. Gallen, SwitzerlandCantonal Hospital St. Gallen, Lung Center, Rorschacherstrasse 95, 9007 St. Gallen, SwitzerlandEmpa, Laboratory for Biomimetic Membranes and Textiles, Lerchenfeldstrasse 5, 9014 St. Gallen, SwitzerlandEmpa, Laboratory for Biomimetic Membranes and Textiles, Lerchenfeldstrasse 5, 9014 St. Gallen, SwitzerlandEmpa, Laboratory for Biomimetic Membranes and Textiles, Lerchenfeldstrasse 5, 9014 St. Gallen, SwitzerlandEmpa, Laboratory for Biomimetic Membranes and Textiles, Lerchenfeldstrasse 5, 9014 St. Gallen, SwitzerlandCantonal Hospital St. Gallen, Lung Center, Rorschacherstrasse 95, 9007 St. Gallen, SwitzerlandCantonal Hospital St. Gallen, Lung Center, Rorschacherstrasse 95, 9007 St. Gallen, SwitzerlandSleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea−hypopnea events per hour of sleep (apnea−hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7−40] <inline-formula> <math display="inline"> <semantics> <msup> <mi mathvariant="normal">h</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math> </inline-formula>. The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.https://www.mdpi.com/1424-8220/20/1/286sleep apneaclassification algorithmsecg signalwearable acquisition deviceheart rate variability analysissupport vector machine |
| spellingShingle | Florent Baty Maximilian Boesch Sandra Widmer Simon Annaheim Piero Fontana Martin Camenzind René M. Rossi Otto D. Schoch Martin H. Brutsche Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device Sensors sleep apnea classification algorithms ecg signal wearable acquisition device heart rate variability analysis support vector machine |
| title | Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device |
| title_full | Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device |
| title_fullStr | Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device |
| title_full_unstemmed | Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device |
| title_short | Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device |
| title_sort | classification of sleep apnea severity by electrocardiogram monitoring using a novel wearable device |
| topic | sleep apnea classification algorithms ecg signal wearable acquisition device heart rate variability analysis support vector machine |
| url | https://www.mdpi.com/1424-8220/20/1/286 |
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