Objective: Most current algorithms for detecting Atrial Fibrillation (AF) rely on Heart Rate Variability (HRV), and only a few studies analyse the variability of Photopletysmography (PPG) waveform. This study aimed to compare morphological features of the PPG curve in patients with AF to those presenting a normal sinus rhythm (NSR) and evaluate their usefulness in AF detection.
Approach: 10-minute PPG signals were obtained from patients with persistent/paroxysmal AF and NSR. Nine morphological parameters (1/ΔT, Pulse Width [PW], Augmentation Index [AI], b/a, e/a, [b-e]/a, Crest Time [CT], Inflection Point Area [IPA], Area and five HRV parameters (Heart rate [HR], Shannon entropy [ShE], root mean square of the successive differences [RMSSD], number of pairs of consecutive systolic peaks [R-R] that differ by more than 50 ms [NN50], standard deviation of the R-R intervals [SDNN]) were calculated.
Main Results: Eighty subjects, including 33 with AF and 47 with NSR were recruited. In univariate analysis five morphological features (1/ΔT, p<0.001; b/a, p<0.001; [b-e]/a, p<0.001; CT, p=0.011 and Area, p<0.001) and all HRV parameters (p=0.01 for HR and p<0.001 for others) were significantly different between the study groups. In the stepwise multivariate model (Area under the curve [AUC] = 0.988 [0.974-1.000]), three morphological parameters (PW, p<0.001; e/a, p=0.011; (b-e)/a, p<0.001) and three of HRV parameters (ShE, p=0.01; NN50, p<0.001, HR, p = 0.01) were significant. 
Significance: There are significant differences between AF and NSR, PPG waveform, which are useful in AF detection algorithm. Moreover adding those features to HRV-based algorithms may improve their specificity and sensitivity.
The quality of spirometry manoeuvres is crucial for correctly interpreting the values of spirometry parameters. A fundamental guideline for proper quality assessment is the ATS/ERS Standards for spirometry, updated in 2019, which describe several start-of-test and end-of-test criteria which can be assessed automatically. However, the spirometry standards also require a visual evaluation of the spirometry curve to determine the spirograms’ acceptability or usability. In this study, we present an automatic algorithm based on a convolutional neural network (CNN) for quality assessment of the spirometry curves as an alternative to manual verification performed by specialists. The algorithm for automatic assessment of spirometry measurements was created using a set of randomly selected 1998 spirograms which met all quantitative criteria defined by ATS/ERS Standards. Each spirogram was annotated as 'confirm' (remaining acceptable or usable status) or 'reject' (change the status to unacceptable) by four pulmonologists, separately for FEV1 and FVC parameters. The database was split into a training (80%) and test set (20%) for developing the CNN classification algorithm. The algorithm was optimised using a cross-validation method. The accuracy, sensitivity and specificity obtained for the algorithm were 92.3%, 92.8% and 90.0% for FEV1 and 92.6%, 93.4% and 89.6% for FVC, respectively. The algorithm provides an opportunity to significantly improve the quality of spirometry tests, especially during unsupervised spirometry. It can also serve as an additional tool in clinical trials to quickly assess the quality of a large group of tests.
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