In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

Hernando, D, Garatachea, N, Almeida, R, Casajús, JA, and Bailón, R. Validation of heart rate monitor Polar RS800 for heart rate variability analysis during exercise. J Strength Cond Res 32(3): 716–725, 2018—Heart rate variability (HRV) analysis during exercise is an interesting noninvasive tool to measure the cardiovascular response to the stress of exercise. Wearable heart rate monitors are a comfortable option to measure interbeat (RR) intervals while doing physical activities. It is necessary to evaluate the agreement between HRV parameters derived from the RR series recorded by wearable devices and those derived from an electrocardiogram (ECG) during dynamic exercise of low to high intensity. Twenty-three male volunteers performed an exercise stress test on a cycle ergometer. Subjects wore a Polar RS800 device, whereas ECG was also recorded simultaneously to extract the reference RR intervals. A time–frequency spectral analysis was performed to extract the instantaneous mean heart rate (HRM), and the power of low-frequency (PLF) and high-frequency (PHF) components, the latter centered on the respiratory frequency. Analysis was done in intervals of different exercise intensity based on oxygen consumption. Linear correlation, reliability, and agreement were computed in each interval. The agreement between the RR series obtained from the Polar device and from the ECG is high throughout the whole test although the shorter the RR is, the more differences there are. Both methods are interchangeable when analyzing HRV at rest. At high exercise intensity, HRM and PLF still presented a high correlation (ρ > 0.8) and excellent reliability and agreement indices (above 0.9). However, the PHF measurements from the Polar showed reliability and agreement coefficients around 0.5 or lower when the level of the exercise increases (for levels of O₂ above 60%).

Background: Validated combined symptom‐medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real‐life data from the MASK‐air^® app to generate and validate hypothesis‐ and data‐driven CSMSs.Methods: We used MASK‐air^® data to assess the concurrent validity, test‐retest reliability and responsiveness of one hypothesis‐driven CSMS (modified CSMS: mCSMS), one mixed hypothesis‐ and data‐driven score (mixed score), and several data‐driven CSMSs. The latter were generated with MASK‐air^® data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK‐air^®, and Work Productivity and Activity Impairment: Allergy Specific [WPAI‐AS]), (ii) quality‐of‐life (EQ‐5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]).Results: We assessed 317,176 days of MASK‐air^® use from 17,780 users aged 16‐90 years, in 25 countries. The mCSMS and the factor analyses‐based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate‐to‐strong correlations with the tested comparators, high test‐retest reliability and moderate‐to‐large responsiveness. Among data‐driven CSMSs, a better performance was observed for cluster analyses‐based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC‐ROC = 0.904) and for the mixed CSMS (AUC‐ROC = 0.820).Conclusion: The mixed CSMS and the cluster‐based CSMSs presented medium‐high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials.

Digital anamorphosis is used to define a distorted image of health and care that may be viewed correctly using digital tools and strategies. MASK digital anamorphosis represents the process used by MASK to develop the digital transformation of health and care in rhinitis. It strengthens the ARIA change management strategy in the prevention and management of airway disease. The MASK strategy is based on validated digital tools. Using the MASK digital tool and the CARAT online enhanced clinical framework, solutions for practical steps of digital enhancement of care are proposed.