Identification of atrial fibrillation episodes using a camera as contactless sensor

Identification of paroxysmal atrial fibrillation (AF) can be difficult and undiagnosed AF patients are at high risk of cardioembolic stroke or other complications associated with AF. The aim of this study is to analyze the video photoplethysmografic (vPPG) signal obtained from a videocamera to explore the possibility of discriminating AF from normal sinus rhythm (NSR) and other arrhythmias (ARR). We acquired 24 3-min long face-videos (8 for each rhythm) using an industrial camera. After preprocessing, vPPG signal was extracted using zero-phase component analysis. Diastolic minima were detected and inter-diastolic series obtained. The signals were characterized by time domain indexes, the sample entropy (SampEn); and the shape similarity index (ShapeSim). The time domain indexes and ShapeSim are significantly different when comparing the group of patients with AF or ARR to subjects in NSR. SampEn is significantly higher in AF than in NSR and ARR. From the shape analysis, it can be noted that waves in NSR are more similar than in AF. These preliminary results show the capability of different indexes to capture differences among AF, ARR and NSR. Further studies will help in assessing the performance of the vPPG signal to screen general population

Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration

BACKGROUND: The autonomic nervous system (ANS) plays an important role in the genesis and maintenance of atrial fibrillation (AF), but quantification of its electrophysiologic effects is extremely complex and difficult. Aim of the study was to evaluate the capability of linear and non-linear indexes to capture the fine changing dynamics of atrial signals and local atrial period (LAP) series during adrenergic activation induced by isoproterenol (a sympathomimetic drug) infusion. METHODS: Nine patients with paroxysmal or persistent AF (aged 60 ± 6) underwent electrophysiological study in which isoproterenol was administered to patients. Atrial electrograms were acquired during i) sinus rhythm (SR); ii) sinus rhythm during isoproterenol (SRISO) administration; iii) atrial fibrillation (AF) and iv) atrial fibrillation during isoproterenol (AFISO) administration. The level of organization between two electrograms was assessed by the synchronization index (S), whereas the degree of recurrence of a pattern in a signal was defined by the regularity index (R). In addition, the level of predictability (LP) and regularity of LAP series were computed. RESULTS: LAP series analysis shows a reduction of both LP and R index during isoproterenol infusion in SR and AF (R(SR )= 0.75 ± 0.07 R(SRISO )= 0.69 ± 0.10, p < 0.0001; R(AF )= 0.31 ± 0.08 R(AFISO )= 0.26 ± 0.09, p < 0.0001; LP(SR )= 99.99 ± 0.001 LP(SRISO )= 99.97 ± 0.03, p < 0.0001; LP(AF )= 69.46 ± 21.55 LP(AFISO )= 55 ± 24.75; p < 0.0001). Electrograms analysis shows R index reductions both in SR (R(SR )= 0.49 ± 0.08 R(SRISO )= 0.46 ± 0.09 p < 0.0001) and in AF (R(AF )= 0.29 ± 0.09 R(AFISO )= 0.28 ± 0.08 n.s.). CONCLUSIONS: The proposed parameters succeeded in discriminating the subtle changes due to isoproterenol infusion during both the rhythms especially when considering LAP series analysis. The reduced value of analyzed parameters after isoproterenol administration could reflect an important pro-arrhythmic influence of adrenergic activation on favoring maintenance of AF

A method for dynamic subtraction MR imaging of the liver

BACKGROUND: Subtraction of Dynamic Contrast-Enhanced 3D Magnetic Resonance (DCE-MR) volumes can result in images that depict and accurately characterize a variety of liver lesions. However, the diagnostic utility of subtraction images depends on the extent of co-registration between non-enhanced and enhanced volumes. Movement of liver structures during acquisition must be corrected prior to subtraction. Currently available methods are computer intensive. We report a new method for the dynamic subtraction of MR liver images that does not require excessive computer time. METHODS: Nineteen consecutive patients (median age 45 years; range 37–67) were evaluated by VIBE T1-weighted sequences (TR 5.2 ms, TE 2.6 ms, flip angle 20°, slice thickness 1.5 mm) acquired before and 45s after contrast injection. Acquisition parameters were optimized for best portal system enhancement. Pre and post-contrast liver volumes were realigned using our 3D registration method which combines: (a) rigid 3D translation using maximization of normalized mutual information (NMI), and (b) fast 2D non-rigid registration which employs a complex discrete wavelet transform algorithm to maximize pixel phase correlation and perform multiresolution analysis. Registration performance was assessed quantitatively by NMI. RESULTS: The new registration procedure was able to realign liver structures in all 19 patients. NMI increased by about 8% after rigid registration (native vs. rigid registration 0.073 ± 0.031 vs. 0.078 ± 0.031, n.s., paired t-test) and by a further 23% (0.096 ± 0.035 vs. 0.078 ± 0.031, p < 0.001, paired t-test) after non-rigid realignment. The overall average NMI increase was 31%. CONCLUSION: This new method for realigning dynamic contrast-enhanced 3D MR volumes of liver leads to subtraction images that enhance diagnostic possibilities for liver lesions

Standards for data acquisition and software‐based analysis of in vivo electroencephalography recordings from animals. A TASK1‐WG5 report of the AES/ILAE Translational Task Force of the ILAE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/1/epi13909.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/2/epi13909_am.pd

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Advanced spectral methods for detecting dynamic behaviour

none3S. CERUTTI; A.M. BIANCHI.; L. T. MAINARDICerutti, Sergio; Bianchi, ANNA MARIA; Mainardi, Luc

The whale forgetting factor in recursive AR spectral analysis of heart rate variability signals

Spectral parameters extracted from the heart rate variability signal are obtained on a beat-to-beat basis by means of autoregressive recursive identification, In this paper a whale forgetting window is introduced, instead of the classical exponential one, in order to reduce the noise influence on the estimated parameters, After proper simulation it was found that the whale forgetting window markedly reduces the noise in the identification, but maintains a good response to abrupt changes in the signal. The algorithm was thus applied to the analysis of the HRV data recorded during different transient situations in physiological and pathological conditions, The spectral parameters were obtained on a beat-to-beat basis and their trends were smoother and more accurate with respect to the traditional exponential window also in presence of noise or artifacts in the time series (sudden and short time changes, ectopic beats, etc.), without losing the signal variations of physiological interest

Time-varying analysis for the estimation of spectral parameters in cardiovascular variability signals

The frequency domain analysis of the heart rare variability (HRV) signal is actually extended to nonstationary situations in order to quantify the autonomic control during physiological or pathological dynamical phenomena. Different methods of time-frequency representation of a signal have been proposed in literature. In the present paper a recursive implementation of the Autoregressive (AR) spectral identification is described together with application in clinical protocols dealing with myocardial ischemia, both spontaneous or induced by and with episodes of vaso-vagal dipyridamol infusion syncope.