Validating an Algorithm for Automatic Scoring of Inspiratory Flow Limitation Within a Range of Recording Settings

• Published in: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2018; pp. 4788 - 4791
• Main Authors: Camassa, Alessandra, Franciosini, Angelo, Sands, Scott A., Ying Xuan Zhi, Yadollahi, Azadeh, Bianchi, Anna M., Wellman, Andrew, Redline, Susan, Azarbarzin, Ali, Mariani, Sara
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2018
• Subjects: Classification algorithms; Feature extraction; Filtering; Filtering algorithms; Sleep apnea; Support vector machines; Training
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2018.8513127

Inspiratory Flow Limitation (IFL) is a phenomenon associated with narrowing of the upper airway, preventing an increase in inspiratory airflow despite an elevation in intrathoracic pressure. It has been shown that quantification of IFL might complement information provided by standard indices such as the apnea-hypopnea index (AHI) in characterizing sleep disordered breathing and identifying subclinical disease. Defining guidelines for visual scoring of IFL has been of increasing interest, and automated methods are desirable to avoid inter-scorer variability and allow analysis of large datasets. In addition, as recording instrumentation and practices may vary across hospitals and laboratories, it is useful to assess the influence of the recording parameters on the accuracy of the automated classification. We employed nasal pressure signals recorded as part of polysomnography (PSG) studies in 7 patients. Two experts independently classified approximately 2000 breaths per subject as IFL or non-IFL, and we used the consensus scoring as the gold standard. For each breath, we derived features indicative of the shape and frequency content of the signals and used them to train and validate a Support Vector Machine (SVM) to distinguish IFL from non-IFL breaths. We also assessed the effect of signal filtering (down-sampling and baseline-removal) on classification performance. The performance of the classifier was excellent (accuracy ~93%) for the raw signals (collected at 125 Hz with no filtering), and decreased for increasing high-pass cut-off frequencies (fc = [0.05, 0.1, 0.15, 0.2] Hz) down to 84% for fc= 0.2 Hz and for decreasing sampling rate (fs = [20, 50, 75, 100] Hz) down to ~85% for fs=20 Hz. Loss of performance was minimized when the classifier was re-trained using data with matched filtering characteristics (accuracy > 89%). We can conclude that the SVM feature-based algorithm provides a reliable and efficient tool for breath-by-breath classification.