Airflow estimation from respiratory sounds - Yıldırım, İlhan. (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2018., 2018)
The aim of this study is estimate the respiratory airﬂow and phases from the respiratory sounds recorded at the chest wall. In order to estimate the absolute airﬂow curve, time varying autoregressive (TVAR) model coeﬃcients are used. TVAR coeﬃ cients are calculated with three approaches: windowing based autoregressive modeling, TVAR modeling with basis functions, TVAR modeling with Kalman ﬁlter. Then evolu tion in magnitudes of spectral band is used as an estimation of airﬂow curve. A Wiener ﬁlter approach is presented for fusion of diﬀerent features to estimate the airﬂow curve. Average of correlation coeﬃcients up to 0.75 for absolute airﬂow and 0.72 for airﬂow are achieved. In the second part of this thesis, respiratory phases are estimated using a neural network and the estimated absolute airﬂow curve. TVAR coeﬃcients, Shan non entropy estimate, percentile frequencies, variance, spectral magnitude and kurtosis are used as inputs. Distributions of these features for diﬀerent phases and Kullback Leibler divergence of these distributions are presented. For phase estimation from es timated airﬂow, heuristic methods are used for local minima extraction and selection of the transition points. 97 and 83 milliseconds (3% and 2.6% of average full cycle) of average deviation from true transition point are achieved with neural networks for inspiration to expiration and expiration to inspiration transitions respectively. 120 and 131 milliseconds (3.8% and 4.1% of average full cycle) of average deviation from true transition point are achieved with heuristic methods for inspiration to expiration and expiration to inspiration transitions, respectively.
Keywords: Heart -- Sounds.; Respiratory insufficiency.