Machine learning for automatic identification of thoracoabdominal asynchrony in children

Background The current methods for assessment of thoracoabdominal asynchrony (TAA) require offline analysis on the part of physicians (respiratory inductance plethysmography (RIP)) or require experts for interpretation of the data (sleep apnea detection). Methods To assess synchrony between the thor...

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Veröffentlicht in:	Pediatric research 2021-04, Vol.89 (5), p.1232-1238
Hauptverfasser:	Ratnagiri, Madhavi V., Ryan, Lauren, Strang, Abigail, Heinle, Robert, Rahman, Tariq, Shaffer, Thomas H.
Format:	Artikel
Sprache:	eng
Schlagworte:	Abdomen Abdomen - physiopathology Adolescent Algorithms Automation Child Child, Preschool Clinical Research Article Computer Graphics Humans Machine Learning Medicine Medicine & Public Health Observer Variation Pattern Recognition, Automated Pediatric Surgery Pediatrics Pilot Projects Plethysmography - instrumentation Plethysmography - methods Reproducibility of Results Respiration Respiratory Mechanics Respiratory Rate Signal Processing, Computer-Assisted Sleep Apnea Syndromes - diagnosis Thorax - physiopathology
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