Spectral Analysis of Blood Pressure Variability in Atrial Fibrillation

Abstract Atrial fibrillation (AF) is a common arrhythmia characterized by desynchronization of atrial electrical activity causing a consequent irregular ventricular response. Blood pressure (BP) fluctuates in a complex mode composed of both short-term and long-term variability. In AF, the beat-to-beat variation of BP is increased because of variations in filling time and in contractility. However, a few studies have analysed short-term BP variations in AF being the interest mainly addressed to 24-hour variations. Aim of this study was to describe BP variability spectrum during AF in short-term recordings. Fifteen patients, referred for electrical cardioversion, with persistent AF were included in the study. An harmonic LF component was observable in all patients' BP spectra, even during AF, i.e., in presence of a very irregular RR series. Introduction Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice and it is characterized by an irregular atrial depolarization that causes an irregular but not completely random ventricular rhythm as well In AF, the beat-to-beat variation of blood pressure (BP) is increased because of variations in filling time and in contractility. Only a few studies Aim of the present study was to describe BP variability spectrum during AF in short-term recordings in patients with persistent AF. Methods Study protocol Fifteen patients (9 male, mean age 67 ± 7 years) with persistent AF (median duration 3 months; range 1-12 months) were included in the study. Three orthogonal leads, a periodic reference arterial pressure measurement and a continuous beat-to-beat noninvasive recordings of arterial pressure were obtained with a Task Force Monitor (CNSystem; Austria) recording system. Surface ECG and BP signals were recorded for about 10 minutes before electrical cardioversion. The sampling frequency was 1 kHz for the ECG signal and 100 Hz for the continuous arterial pressure recording. Electrical cardioversion was performed in fasting state during deep sedation with intravenous propofol (1-2 mg/Kg). Biphasic DC shock (Life Pack 12 defibrillator, Medtronic Inc., Minneapolis, USA) was delivered with rising energies when needed, starting from 100 J (single shock in almost all cases). Series extraction An automatic QRS detection algorithm was used to locate R waves on the ECG and an interactive graphic interface allowed the operator to visually identify and correct missed/misdetected beats. During AF, the search for the systolic values cannot be performed looking for the maximum after the R wave, a

Effects of dietary plant polyphenols and seaweed extract mixture on male-rabbit semen: Quality traits and antioxidant markers

Feed additive consisting of polysaccharides from brown seaweeds plus phenolic acid, hydroxycinnamic acids, tannins, and flavonoids from plant extracts, was used as rabbit food supplement. Main aim of the study was to determine the effect of natural mix of marine and freshwater seaweed polyphenols on selected reproductive characteristics of male rabbits during the 90-days experiment. Natural mix was incorporated in feed-pellets for rabbits in two different concentrations – 0.3% (T1 group) and 0.6% (T2 group), compared with a control group (C group). In experimental groups a significant increase of concentration of calcium during first 30 days of supplementation was found. An increase of alanine aminotransferase, glutathione peroxidase and ferric reducing ability of plasma and a decrease in aspartate aminotransferase after 90 days were recorded in the same groups. Except for that we noticed decrease of semen distance of curved line and velocity of curved line after 30 days though only while being supplemented with 0.6% proportion of seaweed polyphenols in feed mixture. Based on the results it can be stated that the natural mix in the tested levels do not show adverse effect on male rabbit reproductive parameters, and an improvement of antioxidant status was observed. The feed additives can have a very important effect on growth, health and development of animals in general as it supplies the with the much-needed minerals, nutritional substances and antioxidants, on which we focused in our study

Characterization of a Test for Invasive Breast Cancer Using X-ray Diffraction of Hair—Results of a Clinical Trial

Objective: To assess the performance of a test for breast cancer utilizing synchrotron x-ray diffraction analysis of scalp hair from women undergoing diagnostic radiology assessment. Design and Setting: A double-blinded clinical trial of women who attended diagnostic radiology clinics in Australia. Patients: 1796 women referred for diagnostic radiology, with no previous history of cancer. Main Outcome Measures: Sensitivity, specificity and accuracy of the hair test analysis compared to the gold standard of imaging followed by biopsy where indicated. Results: The hair-based assay had an overall accuracy of >77% and a negative predictive value of 99%. For all women, the sensitivity of both mammography and x-ray diffraction alone was 64%, but when used together the sensitivity rose to 86%. The sensitivity of the hair test for women under the age of 70 was 74%. Conclusion: In this large population trial the association between the presence of breast cancer and an altered hair fibre X-ray diffraction pattern previously reported has been confirmed. It appears that mammography and X-ray diffraction of hair detect different populations of breast cancers, and are synergistic when used together

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

Turbulence in Rivers

The study of turbulence has always been a challenge for scientists working on geophysical flows. Turbulent flows are common in nature and have an important role in geophysical disciplines such as river morphology, landscape modeling, atmospheric dynamics and ocean currents. At present, new measurement and observation techniques suitable for fieldwork can be combined with laboratory and theoretical work to advance the understanding of river processes. Nevertheless, despite more than a century of attempts to correctly formalize turbulent flows, much still remains to be done by researchers and engineers working in hydraulics and fluid mechanics. In this contribution we introduce a general framework for the analysis of river turbulence. We revisit some findings and theoretical frameworks and provide a critical analysis of where the study of turbulence is important and how to include detailed information of this in the analysis of fluvial processes. We also provide a perspective of some general aspects that are essential for researchers/ practitioners addressing the subject for the first time. Furthermore, we show some results of interest to scientists and engineers working on river flows

A Poincaré Image-Based Detector of ECG Segments Containing Atrial and Ventricular Beats

An electrocardiogram (ECG) classifier for the detection of ECG segments containing atrial or ventricular (A/V) beats could ease in the detection of premature atrial complexes (PACs) and by so, the study of their relationship with atrial fibrillation (AF) and stroke. In this work such a classifier is presented based on convolutional neural networks (CNN) and the RR and dRR interval representation on Poincaré Images. Two PhysioNet open-source databases containing beat annotations were used. ECG signals were divided into 30-beat segments with a 50% overlap. Each segment was then transformed into a Poincaré Image. A total of 381151 and 62142 Poincaré Images were computed for normal (N) and A/V segments. RR, dRR and both types of Poincaré Images combined were evaluated as inputs to the CNN. The CNN was trained following a patient-wise train-test division (i.e., no patient was included both in the train and test set) in a 10-fold cross-validation. The patient-wise median and interquartile range accuracy, sensitivity and positive predictive values were 97.90 (94.49 - 99.28), 96.03 (89.67 - 98.76) and 91.91 (70.87 - 99.24), respectively for RR input. No statistical significant differences in performance were found among the three types of Poincaré Images input. Results suggest the present methodology manages to distinguish among N and A/V with high precision

ECG Morphological Decomposition for Automatic Rhythm Identification

Manual rhythm classification in 12-lead ECGs is time-consuming and operator-biased. We present an automatic ECG classifier using CinC's 2020 challenge dataset. In the first phase of the Challenge, 9 categories were targeted with an ensemble of 4 classifiers. In the second phase, 7 classifiers were implemented to detect 24 cardiac electrophysiological disorders. Five classifiers identified abnormalities in different specific regions of the heart's conducting system. Two classifiers were dedicated to detect premature atrial and ventricular contractions. The methodology is based on the creation of rhythm-specific intra and inter-patient templates. Firstly, signals were divided into 6 regions of interests. Secondly, for each region, intra-patient models and inter-patient rhythm-specific models were computed. The distances from each intra-patient model to each rhythm-specific inter-patient model as well as heart rate variability features and Global Electric Heterogeneity features were introduced into the classifiers. After a 10-fold cross-validation, for the provided training data in the first phase an accuracy of 94.4%±0.4, and a Challenge metric of 0.644±0.031 were obtained, whereas in the second phase an accuracy and Challenge metric of 15.0± 1.0 % and 0.030 ±0..009 were obtained