Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

Mobile phones offer the possibility to monitor and track health parameters. Our aim was to test the feasibility and accuracy of measuring beat-to-beat heart rate using smartphone accelerometers by recording the vibrations generated by the heart during its function and transmitted to the chest wall, i.e. the so-called seismocardiographic signal (SCG). Methods: 9 healthy male volunteers were studied in supine (SUP) and in standing (ST) posture. A smartphone (iPhone6, Apple) was positioned on the thorax (POS1) to acquire SCG signal. While supine, a second smartphone was positioned on the navel (POS2). The SCG signal was recorded for 3 minutes during spontaneous respiration, synchronous with 3-leads ECG. Using a fully automated algorithm based on amplitude thresholding after rectification, the characteristic peak of the SCG signal (IVC) was detected and used to compute beat-to-beat heart duration, to be compared with the corresponding RR intervals extracted from the ECG. Results: A 100% feasibility of the approach resulted for POS1 in SUP, while 89% in POS2, and 78% for POS1 in ST. In supine, for each smartphones' position, the automated algorithm correctly identified the cardiac beats with >98% accuracy. Linear correlation (r²) with RR was very high (>0.98) in each posture and position, with no bias and narrow limits of agreement. Conclusions: The obtained results proved the feasibility of the proposed approach and the robustness of the applied algorithm in measuring the beat-to-beat heart rate from smartphone-derived SCG, with high accuracy compared to conventional ECG-derived measure.

The feasibility of measuring stress-related parameters by ultra-short variability (USV) indices calculated from the ballistocardiographic signal acquired by the mobile phone accelerometers (m-BCG) positioned on the navel was tested, and its accuracy compared with gold standard ECG-derived indices. The m-BCG was acquired in six healthy volunteers while in supine position, during spontaneous breathing (CTRL) and during 1 minute of mental stress (MS) induced by arithmetic serial subtraction task. Beat occurrence was independently and automatically extracted from both ECG and m-BCG signals, to compute USV parameters in 30 s intervals, during both the CTRL and MS. Linear regression and Bland-Altman analyses between RR series and m-BCG derived beat-to-beat measurements (JJ series) showed very high correlation (r ² >0.97), no bias, and narrow limits of agreement (±2SD <; ±34 ms) for both CTRL and MS. A significant decrease (p=0.03 Wilcoxon test) in beat duration, SDNN and RMSSD was found in MS compared to CTRL, in both RR and JJ variability series, underlying the ability of m-BCG in capturing the decrease in parasympathetic tone in agreement with the induced stimulus.

Ballistocardiography (BCG), measuring the heart beat-induced mass movements of the body generated by forces associated to heart contraction, has been recently reconsidered as a useful non-invasive technique to characterize cardiac performance, potentially offering a simple, efficient and affordable solution for e-health monitoring. Our aim was to extract heart rate variability indices from BCG signal (B-HRV), and to evaluate their correlation with ECG-derived (E-HRV) ones, both in time and frequency domains.

Objective: The aim of this work was to evaluate the potential changes in the ballistocardiogram (BCG) signal induced by different respiratory patterns and posture, by using the dynamic time warping (DTW) technique. Approach: BCG signals were recorded in a group of 20 healthy volunteers, simultaneously with an electrocardiogram (ECG). Two recordings, one in a supine (SUP) and one in a standing (ST) position, including spontaneous breathing, two 1 min apneas (at full and empty-lungs, respectively) and 30 s of Valsalva, were analyzed. A warped averaged waveform was computed for each phase, from which amplitude and temporal parameters were extracted to characterize each condition. Main results: Variations were found in both amplitude and duration of BCG-derived parameters among manoeuvres, especially when compared to spontaneous breathing, suggesting a complex interaction between intra-thoracic pressure changes acting on venous return, together with the autonomic nervous system modulation on heart rate. The effect of a hydrostatic pressure gradient elicited by postural conditions was also evident. Significance: Posture and respiratory manoeuvres affect the BCG signal in different ways, probably as a result of changes induced in preload and afterload. This supports the need to define separate normality ranges for each posture and/or breathing conditions, as well as the importance of applying specific manoeuvres to highlight any pathological response in the computed BCG parameters.