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 thorax and abdomen, the movements of the two compartments during quiet breathing were measured using pneuRIP. Fifty-one recordings were obtained: 20 were used to train a machine-learning (ML) model with elastic-net regularization, and 31 were used to test the model's performance. Two feature sets were explored: (1) phase difference (ɸ) between the thoracic and abdominal signals and (2) inverse cumulative percentage (ICP), which is an alternate measure of data distribution. To compute accuracy of training, the model outcomes were compared with five experts' assessments.
Results: Accuracies of 61.3% and 90.3% were obtained using ɸ and ICP features, respectively. The inter-rater reliability (i.r.r.) of the assessments of experts was 0.402 and 0.684 when they used ɸ and ICP to identify TAA, respectively.
Conclusions: With this pilot study, we show the efficacy of the ICP feature and ML in developing an accurate automated approach to identifying TAA that reduces time and effort for diagnosis. ICP also helped improve consensus among experts.
Impact: Our article presents an automated approach to identifying thoracic abdominal asynchrony using machine learning and the pneuRIP device. It also shows how a modified statistical measure of cumulative frequency can be used to visualize the progression of the pulmonary functionality along time. The pulmonary testing method we developed gives patients and doctors a noninvasive and easy to administer and diagnose approach. It can be administered remotely, and alerts can be transmitted to the physician. Further, the test can also be used to monitor and assess pulmonary function continuously for prolonged periods, if needed.