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Automated analysis of paradoxical ribcage motion during sleep in infants
K A Brown1, R Platt, J H T Bates
1Department of Anesthesia, McGill University Health Centre/Montreal Children's Hospital, 2300 Tupper St., Room C-1119, Montreal, Quebec, Canada H3H 1P3. karen.brown@mcgill.ca
Insights
Automating thoracoabdominal asynchrony (TAS) detection in infants is crucial. A new recursive least squares method accurately identifies TAS using ribcage and abdominal movement signals, improving upon traditional methods.
Area of Science:
- Biomedical Engineering
- Pediatric Respiratory Physiology
- Signal Processing
Background:
- Thoracoabdominal asynchrony (TAS) detection currently relies on manual visual coding of ribcage (RC) and abdominal (AB) movement recordings.
- Manual TAS detection is time-consuming and prone to accuracy issues.
- Automating TAS detection could improve efficiency and reliability in clinical settings.
Purpose of the Study:
- To develop and validate a novel automated method for identifying thoracoabdominal asynchrony (TAS) in infants.
- To compare the accuracy of the novel method against a conventional measure (thoracic delay).
Main Methods:
- Recorded ribcage (RC) and abdominal (AB) movement signals from 15 infants using inductance plethysmography.
- Applied a novel analysis using recursive linear regression (RC = alphaAB + beta) to 10-second epochs.
- Calculated synchrony/asynchrony based on the sign of the 'alpha' signal over time.
- Compared the novel method's results with the conventional thoracic delay (TD) measure.
Main Results:
- The novel recursive least squares method achieved a 99% positive predictive value when compared to the thoracic delay (TD) method.
- The method accurately identified epochs corresponding to thoracoabdominal asynchrony (TAS).
Conclusions:
- The developed recursive least squares method accurately automates the detection of thoracoabdominal asynchrony (TAS) in infant breathing patterns.
- This automated approach shows potential for clinical application in improving TAS detection accuracy and efficiency.
Abstract:
Identification of thoracoabdominal asynchrony (TAS) during breathing is currently detected by visual coding of records of ribcage (RC) and abdominal (AB) movements. There is thus a need to automate this process in order to save time and improve TAS detection accuracy. We studied 15 infants of 39-49 weeks postconceptional age. RC and AB signals were recorded continuously by inductance plethysmography for 4-24 hr immediately after herniorraphy. In our novel analysis approach, the records were divided into 10 sec epochs, and the equation RC = alphaAB + beta was fit to each epoch, using recursive linear regression with an exponential memory time constant of 1 and 2 sec. This yielded 10 sec signals for alpha corresponding to each epoch. The fraction of time that each alpha signal was positive was taken as a measure of synchrony between RC and AB for that epoch, while asynchrony was indicated by the fraction of time the signal was negative. We also assessed synchrony and asynchrony using a conventional measure known as thoracic delay (TD), which is based on the degree to which the peaks in RC and AB are coincident in time. Using TD as the basis of comparison, we found that our new recursive least squares method gave a positive predictive value of 99%. We conclude that our recursive least squares method is able to accurately identify portions of the RC and AB records that correspond to TAS, and we speculate that it may be useful in automating detection of TAS.
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