Top PDF Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Atrial fibrillation is usually diagnosed upon examination of a body surface ECG signal in which the absence of P waves, and a rapid ventricular response, would mark the occurrence of an Atrial Fibrillation episode. However, the P wave can be easily obscured in the presence of noise, as it usually has a very low amplitude when compared to the R waves. Because of this reason, the automatic detection of atrial fibrillation is currently divided in two main categories: the analysis of P waves [25,26,27,28], and the analysis of RR intervals (interval between consecutive peaks of the QRS complex) [29,30,31,32,33]. Given that the P wave would only be visible in ECG signals, as it depicts the electrical impulse that triggers the atria, and that such wave is not present in the blood volume pulsations recorded via plethysmography (PPG, VPG), we will focus on the atrial fibrillation detection methods that rely on the analysis of RR intervals.
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Atrial fibrillation detection by heart rate variability in Poincare plot

Atrial fibrillation detection by heart rate variability in Poincare plot

There are lots of studies about detecting AFib. Xu et al. chose five feature parameters which were input regularity, input atrial rate, energy distribution, time interval corre- sponding to zero amplitude signal, and number of points reaching baseline. They used Bayesian discriminator to classify the input data as one of sinus rhythm, AFib or atrial flutter [10]. Petrucci et al. used two histograms which were calculated from the inter-beat intervals. One histogram consisted of differences between two successive inter-beat intervals and the other histogram consisted of normalized deviations from mean value of the inter-beat intervals. They calculated distribution widths from these histograms to discriminate AFib from non-AFib [11]. Kikillus et al. made a Poincaré plot from inter-beat inter- vals and estimated density of points in each segment of Poincaré plot. They calculated an indicator of AFib from standard deviation of temporal differences of the consec- utive inter-beat intervals [12]. Thuraisingham used wave- let method to obtain a filtered time series from the input ECG. He calculated the standard deviation of the time series and the standard deviation of successive differences, and the length of the ellipse that characterized the Poin- caré plot. He used these indicators to discriminate AFib from non-AFib [13]. Shouldice et al. made feature vectors from inter-beat intervals, and then applied Fisher's linear discriminant method to estimate the likelihood of a block of inter-beat intervals containing the paroxysmal AFib [14]. Kikillus et al. tried to detect AFib using a method of neural network. They calculated 25 parameters of time domain, frequency and non-linear domain, with which they applied two neural networks to decide whether the input ECG implied AFib [15].
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Automated detection of atrial fibrillation using long short term memory network with RR interval signals

Automated detection of atrial fibrillation using long short term memory network with RR interval signals

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxys- mal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection.
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Atrial fibrillation following cardiac surgery: risk analysis and long-term survival

Atrial fibrillation following cardiac surgery: risk analysis and long-term survival

The main factors associated with an increased risk of POAF were age, the complexity of the procedure, a his- tory of heart failure, and low EF in addition to a higher EuroSCORE—all factors that have been described in pre- vious studies. This information was used to construct a likelihood table for the development POAF, based on age, operation type, and EuroSCORE (Figure 1). One of the main strengths of our model is the complete dataset that was available for its construction as well as its simplicity, making it a feasible tool for use in clinical practice. The main drawback was the observational de- sign of our study and the low number of study patients, even though incidence was higher than in most previous studies, possibly due to higher pick-up rates. Using the entire population to create the risk model was consid- ered necessary to obtain sufficient power. There is a possibility of overfitting—that the model describes the risk in the population studied adequately but fails to predict the risk in other patient cohorts. The model would be improved by testing out its prediction cap- abilities with an Icelandic validation cohort, ideally in a prospective manner. We believe that a risk score would help to identify patients who might be candi- dates for prophylactic therapy or close electrocardio- graphic monitoring.
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ATRIAL FIBRILLATION AND ANAESTHESIA

ATRIAL FIBRILLATION AND ANAESTHESIA

Examine for adverse signs and clinical evidence of low cardiac output. Pallor, sweating, cold and clammy extremities are secondary to sympathetic activity. Hypotension may develop. If awake, observe for decreasing level of consciousness. A very high ventricular response rate (>150) can reduce coronary blood flow and cause myocardial ischemia with chest pain. Look for signs of heart failure: pulmonary oedema (left sided failure) raised JVP and hepatic engorgement (right sided failure). 6

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EmergencyKT: Atrial Fibrillation

EmergencyKT: Atrial Fibrillation

Kister JP et al. The Effect of Low-Dose Warfarin on the Risk of Stroke in Patients with Nonrheumatic Atrial Fibrillation: The Boston Area Anticoagulation Trial for Atrial Fibrillation. N Engl J Med 1990; 323:1505-1511 Khan IA, Mehta NJ, Gowda RM. Amiodarone for pharmacological cardioversion of recent-onset atrial fibrillation. Int J Cardiol. 2003;89:239–48

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Management of atrial fibrillation

Management of atrial fibrillation

Another well-used and validated system, the CHADS2 risk stratification scheme (Gage et al 2001) gives a numerical score to each of five risk factors (congestive heart failure, hypertension, age ≥75 years, diabetes and stroke/TIA, the latter receiving a score of 2). The total score (0–6) equates to a recognized future stroke risk (where a CHADS2 score of 0 would define a person as being at “low” risk (and suitable for aspirin) and ≥3 as a “high risk” (and warfarin is recom- mended). However, AF patients with previous stroke, TIA, or thromboembolism are considered to be at high risk of a further stroke or thromboembolic event. However using the CHADS 2 , such patients with this risk factor alone would only have a total CHADS 2 score of 2, which would classify them as “moderate’ risk”.
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Metformin therapy and postoperative atrial fibrillation in diabetic patients after cardiac surgery

Metformin therapy and postoperative atrial fibrillation in diabetic patients after cardiac surgery

Data extracted from the STS database included patient demographics (age, sex, race, body mass index (BMI), al- cohol use), medical characteristics including hypertension, dyslipidemia, lung disease, peripheral artery disease, prior heart failure, prior myocardial infarction, chronic kidney disease (estimated glomerular filtration rate (eGFR) < 60 mL/min), glycated hemoglobin (HbA1C), and mitral valve disease/stenosis. We also collected preoperative and postoperative medication use including angiotensin- converting enzyme (ACE), angiotensin receptor blockers (ARB), acetylsalicylic acid (ASA), other anti-platelet, beta blocker lipid-lowering agents, steroids, vitamins C and E, amiodarone, and magnesium sulfate. Lastly, we extracted cardiac characteristics data such as active myocardial in- farction (STEMI), coronary artery bypass graft (CABG), valve surgery, cross-clamp time, explant position, intra- aortic balloon pump, surgery status, and resuscitation.
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Atrial Fibrillation in Children

Atrial Fibrillation in Children

The sick-sinus syndrome developed in five patients (cases 4, 19, 21, 24, and 25), character- ized by the following bradyarrhythmias and tachyarrhythmias: sinus arrest and junctional brad[r]

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Atrial Fibrillation and Hyperthyroidism

Atrial Fibrillation and Hyperthyroidism

Elderly patients with thyrotoxicosis and atrial fibrillation and those with other risk factors for thromboembolism have significantly increased risk for arterial thromboembolism and anticoagulant treatment is indicated. Elderly patients are particularly at risk for hemorrhagic complications and hence close monitoring of prothrombin time is required in elderly patients on warfarin. Antiplatelet agents like aspirin may afford some protection against cardioembolic stroke in patients with atrial fibrillation, although these are more effectively prevented by anticoagulation 33 .
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Rivaroxaban in atrial fibrillation

Rivaroxaban in atrial fibrillation

Abstract: Warfarin is the traditional therapeutic option available to manage thromboembolic risk in atrial fibrillation. The hemorrhagic risk with warfarin depends mainly on the interna- tional normalized ratio (INR). Data from randomized controlled trials show that patients have a therapeutic INR (2.00–3.00) only 61%–68% of the time while taking warfarin, and this target is sometimes hard to establish. Many compounds have been developed in order to optimize the profile of oral anticoagulants. We focus on one of them, rivaroxaban, comparing it with novel alternatives, ie, dabigatran and apixaban. The indication for rivaroxaban in nonvalvular atrial fibrillation was evaluated in ROCKET-AF (Rivaroxaban-once daily, Oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation). In this trial, rivaroxaban was associated with a 12% reduction in the incidence of the primary endpoint compared with warfarin (hazard ratio 0.88; 95% confidence interval [CI] 0.74–1.03; P , 0.001 for noninferiority and P = 0.12 for superiority). However, patients remained in the therapeutic range for INR only 55% of the time, which is less than that in RE-LY (the Randomized Evaluation of Long-Term Anticoagulation Therapy, 64%) and in the ARISTOTLE trial (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation, 66%). This shorter time spent in the therapeutic range has been one of the main criticisms of the ROCKET-AF trial, but could actually reflect what happens in real life. In addition, rivaroxaban exhibits good pharmacokinetic and pharmacoeconomic properties. Novel anticoagulants are a viable and commercially available alternative to vitamin K antagonists nowadays for the prevention of thromboembolic complications in atrial fibrillation. Rivaroxaban is an attractive alternative, but the true picture of this novel compound in atrial fibrillation will only become available with more widespread use.
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Life-threatening intestinal ischaemia and necrosis in a patient with cardiac arrest and atrial fibrillation

Life-threatening intestinal ischaemia and necrosis in a patient with cardiac arrest and atrial fibrillation

The patient was subsequently transferred to the cardiology ward, where he was carefully questioned and examined by the ward doctor. The patient only complained of pain from his ribs, and no other symptoms were present at that time. The ECG revealed atrial fibrillation with a ventricular rate of 100 beats/min, no ST segment changes and a normal QT interval. The patient had no previous history of atrial fibrillation and had no cardiac-related symptoms before the cardiac arrest, which made it difficult to estimate when the atrial fibrillation had begun. Bedside echocardiography revealed a near normal ejection fraction, no valvular abnormities and no pericardial effusion. There was a pre-existing hypokinetic area in the inferolateral segments known from earlier echocardiography 10 years previously after the coronary bypass operation.
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Early Experience Using a Left Atrial Appendage Occlusion Device in Patients with Atrial Fibrillation

Early Experience Using a Left Atrial Appendage Occlusion Device in Patients with Atrial Fibrillation

Fig. 2. Intra-procedural TEE images before (A, B and C) and after (D) deployment of LAA occlusion device. Diameter of LAA ostium and depth of LAA were measured from 4 different angles of TEE images to determine the appropriate size of WATCHMAN device. (D) Successful deployment of device should be confirmed using a tug test and color Doppler. (E) RAO 45° fluoroscopic view after deployment of WATCHMAN device. (F) Eight week follow-up 3-D TEE showed complete sealing off of LAA by WATCHMAN device. TEE, trans-esoph- ageal echocardiography; LAA, left atrial appendage; RAO, right anterior oblique.
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Phenotypic description of cardiac findings in a population of Dogue de Bordeaux with an emphasis on atrial fibrillation

Phenotypic description of cardiac findings in a population of Dogue de Bordeaux with an emphasis on atrial fibrillation

timing ECG and attending clinicians’ records. Holter data were quantitatively analysed by Laboratory Corporations of America, Ambulatory Monitoring Services. For initial analysis, AF and supraventricular tachycardia (SVT) were described collectively as ‘supraventricular arrhythmia’ (Santilli et al., 2008). Atrial fibrillation was diagnosed when QRS complexes occurred without periodicity in the absence of P waves or flutter waves (Miller et al., 1999). Supraventricular tachycardia was defined as three or more QRS complexes < 0.07 s duration, occurring regularly at an instantaneous rate > 160 beats per min (bpm), paroxysmally or persistently, and deemed to be physiologically inappropriate by the attending clinician or on review of Holter data with reference to the Holter activity diary. Dogs in sinus rhythm with no evidence of paroxysmal supraventricular arrhythmia on review were classed as ‘sinus rhythm’. Ventricular arrhythmia was described as ‘isolated’ if only individual ventricular premature complexes (VPCs) were identified, or ‘complex’ if couplets, triplets, bigeminy, trigeminy and/or ventricular tachycardia were documented. Dogs with only isolated VPCs on Holter were dichotomised with a cut off of 100 VPCs/24 h.
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FIBRILLATION ATRIAL 12 INTERNATIONAL. Atrial Fibrillation and Heart Failure: MEETING. Preliminary Program

FIBRILLATION ATRIAL 12 INTERNATIONAL. Atrial Fibrillation and Heart Failure: MEETING. Preliminary Program

A special awards 1.500,00 euro gross will be given to the “Best Abstract” works selected by the Abstract Selection Committee between accepted oral communications and posters. The winne[r]

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Contemporary therapy of atrial fibrillation

Contemporary therapy of atrial fibrillation

Atrial fibrillation (AF) is estimated that by 2010, ap- proximately 2.6 million people will be affected in USA; by 2050, that number may increase to 10 million pa- tients. Generally, rate control alone is reasonable in some AF patients, especially asymptomatic patients. Restoration and maintenance of sinus rhythm (SR) may be achieved by means of cardioversion, drugs or/ and catheter ablation. Pharmacological therapy can be useful to maintain SR and prevent tachycardia- induced cardiomyopathy. All patients with AF re- gardless of whether a rhythm or rate control strategy recommend anticoagulant, antiplatelet or both com- bined therapy for prevention of thromboembolism, except those with lone AF or contraindications. Drug selection should be based upon the absolute risk of stroke, bleeding, the relative risk and benefit for a given patient. Biventricular pacing may overcome many of the adverse hemodynamic effects associated with RV pacing alone. A target individual ectopic foci ablation within the pulmonary vein (PV) has evolved to circumferential electrical isolation of the entire PV musculature. Cavotricuspid isthmus should be con- sidered as first-line therapy for patients with typical atrial flutter. Completely non-fluoroscopic ablation guided by Real-Time Magnetic Resonance Imaging (RTMRI) using a steerable and non-ferromagnetic catheter is a promising novel technology in interven- tional electrophysiology.
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Is atrial fibrillation a risk factor for in-hospital cardiac arrest?: a Swedish retrospective cohort study

Is atrial fibrillation a risk factor for in-hospital cardiac arrest?: a Swedish retrospective cohort study

or VF) in 15% of those with AF and 12% of those without (p=0.33, table 1). No significant difference regarding 30-day survival after the IHCA was found between those with compared with without AF. Patients with AF had a crude and adjusted significantly higher association with Figure 1 Portion of patients with atrial fibrillation (International Classification of Diseases, 10th revision code I48) among adult hospitalised patients at Karolinska University Hospital 1 January 2014 to 31 December 2015.

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Post operative atrial fibrillation

Post operative atrial fibrillation

fibrillation is sustained by m ultiple re-entrant circuits and that random pathw ays are determ ined by atrial refractoriness, excitability and conduction properties. In m ost cases these re-entrant circuits are constantly arising, colliding and being extinguished. It requires a critical m ass o f atrial tissue to sustain the arrhythm ia and adjacent atrial regions w ith different refractory periods m ay increase re-entry. A nother im portant concept is the presence o f “drivers” , these are areas that generate a rapid rhythm o f a very short cycle length, the atria are unable to respond to this in a one to one fashion and so the result is fibrillatory conduction. These atrial re-entry circuits arc often referred to as “rotors” and can be self-sustaining. A vortex o f electrical w aves (spiral waves) can arise and precipitate and propagate AF. The pulm onary veins and indeed other structures such as the vena cava, ligam ent o f M arshall and the thoracic veins have also been im plicated in AF. A sleeve o f atrial tissue often extends into these structures and a rapidly firing focus has been show n to precipitate AF. Slow ing in atrial conduction m ay also increase re-entry and this is supported by the fact that an increase in pre-operative P-w ave duration on signal averaged ECG is a risk factor for the developm ent o f post-operative A F (35,36). A short cycle w avelength and an increase in atrial size m ay enhance atrial vulnerability to A F (37). Atrial ischaem ia, traum a, stretch, sym pathetic activation or inflam m ation m ay alter atrial refractoriness, shorten cycle length o r slow atrial conduction and hence precipitate the arrhythm ia.
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Monitoring Atrial Fibrillation using PPG Signals and a Smartphone

Monitoring Atrial Fibrillation using PPG Signals and a Smartphone

Nowadays, several works suggest using smartphones to detect some types of ar- rhythmia. In [16] a smartphone application that detects irregular pulse using an iPh- one 4S was developed. Namely, the application makes use of the iPhone’s camera to obtain pulsatile time series recordings, which were later analyzed using two statistical methods: root mean square of successive RR difference (RMSSD/mean) and Shannon entropy (ShE). In the end, the two methods turned out to be significant in terms of AF detection. In [17], the authors employed an iPhone 4S camera to record patient pulse. Then, their algorithm takes into account pulse rise and fall times to detect AF. The proposal is able to monitor heart rate and calculate its variations, which are then ana- lyzed using t-tests to either accept or reject a diagnosis. Similarly, the method is par- ticularly useful for detecting bigeminy, trigeminy, and/or unpredictable heartbeat that may lead to AF. Finally, in [18], the authors proposed an initiative that records patient facial videos; then, the video images are processed on a computer using the Kanade- Lukas-Tomasi (KLT) algorithm and a series of digital filters. The goal is to obtain heart rate parameters, including heart rate variations. It is a non-intrusive solution, but it is not wearable.
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Electrophysiology. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and rhythm control using cardioversion

Electrophysiology. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and rhythm control using cardioversion

in sinus rhythm necessitating pacemaker implantation; 1 patient was known to be with a right bundle branch block), and 1 on flecainide (transient AV-nodal block directly after electrical cardioversion from AF; flecai- nide was discontinued). The patient on flecainide also used digoxin; the 2 patients on amiodarone did not use other AV-nodal–blocking agents. One patient had a rapid hemodynamically significant AV conduction during atrial flutter on flecainide; 1 had suffered from exercise-induced ventricular tachycardia during inhos- pital loading, thereafter, flecainide was discontinued; 1 had drug-induced heart failure on flecainide; and 1 had sick sinus syndrome on amiodarone.
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