Prolonged monitoring is more likely to result in an accurate diagnosis of atrial fibrillation patients than intermittent or short-term monitoring. In this study, we present an implantable ECG sensor to monitor atrial fibrillation patients in real time. The developed implantable sensor is composed of a micro controller unit, analog to digital converter, signal transmitter, antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these signals to the external receiver that is carried by the patient. The sensor continuously transmits signals, so its battery consumption rate is extremely high. To overcome this problem, we consider using a wireless power transmission module in the sensor module. This module helps the sensor charge power wirelessly without holding the battery in the body. The size of the integrated sensor is approximately 0.12 x 1.18 x 0.19 inch. This sensor size is appropriate enough for cardiologists to insert the sensor into patients without the need for a major surgery. The data sampling rate was 300 samples/sec, and the frequency was 430 Hz for signal and power transmission.
Atrial Fibrillation ; Chronic Disease* ; Diagnosis ; Electrocardiography ; Electrodes ; Humans

Atrial fibrillation is association a wide range of genetic, metabolic, and environmental causes. The number of patients with atrial fibrillation is increasing exponentially, predominantly due to aging and a variety of heart conditions such as ischemic heart disease and heart failure. Owing to a range of unmet clinical and social needs, atrial fibrillation has become a significant target for research studies. Thus far, research has revealed several important mechanisms related to the pathophysiology, diagnostic, and optimal treatment of atrial fibrillation. In this review, we aim to summarize the current status of research on atrial fibrillation and relate such progress to the European Atrial Fibrillation Guidelines (2016).
Aging ; Atrial Fibrillation* ; Diagnosis* ; Heart ; Heart Failure ; Humans ; Myocardial Ischemia

BACKGROUND AND OBJECTIVES: The diagnosis of paroxysmal atrial fibrillation (PAF) and the prediction of its recurrence are sometimes difficult. There have been several recent studies attempting to detect patients at risk for PAF while in sinus rhythm by using the P wave signal-averaged ECG. We undertook to define an appropriate technique of P wave signal-averaged ECG and to estimate the reproducibility of the test. Additionally, we estimated the usefulness of P wave signal-averaged ECG in patients at risk for PAF. SUBJECTS AND METHODS: Forty-five patients with PAF were included in the study undertaken between March 1997 and June 1998. Twelve-lead surface ECG and P wave signal-averaged ECG were performed in the patients. The total P wave duration was measured by the P wave signal-averaged ECG using P wave template and least-square fit filter. The same process was followed in forty sex- and age-matched controls. RESULTS: The measurement of P wave duration with P wave signal-averaged ECG was highly reproducible. The measured P wave duration showed significant prolongation in the patient group at cutoff frequencies of 20 Hz and 30 Hz (123.6+/-15.3 vs. 114.8+/-14.5 msec, p=0.009 at 20 Hz, 120.1+/-17.8 vs. 107.5+/-18.8 msec, p=0.002 at 30 Hz). An abnormal P wave duration defined as over 120 msec in duration by P wave signal-averaged ECG was able to detect PAF with a sensitivity of 60%, specificity of 73%, positive predictive value of 71%, and a negative predictive value of 62%. CONCLUSION: A prolonged P wave duration as measured by P wave signal-averaging technique may be a simple noninvasive marker of risk for the development of atrial fibrillation.
Atrial Fibrillation* ; Diagnosis ; Electrocardiography* ; Humans ; Recurrence ; Sensitivity and Specificity

An atrial septal aneurysm is well recognized abnormality of uncertain clinical relevance. An intraatrial aneurysm was demonstrated in the fossa ovalis of a 41-year-old woman who suffered an episode of cerebellar infarction with recurrent atrial fibrillation. The disorder is rarely treated surgically. Most patients with this condition are given life-long anticoagulation, a treatment that may have serious complications. We report a rare case of atrial septal aneurysm with recurrent atrial fibrillation and cerebellar infarction which receiving an appropriate diagnosis and curative treatment.
Adult ; Aneurysm* ; Atrial Fibrillation* ; Diagnosis ; Female ; Humans ; Infarction*

Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.
Atrial Fibrillation ; diagnosis ; Computer Systems ; Heart Rate ; Humans ; Machine Learning

BACKGROUNDRecent observational studies have shown that patients with higher Killips score (>I) have higher risk of new-onset atrial fibrillation (NOAF) following acute myocardial infarction (AMI), while others drew a neutral conclusion. The ultimate predictive value of high Killips class on NOAF remained obscure.

METHODSPubMed, Web of Science, China National Knowledge Infrastructure, and the Cochrane Controlled Trials Register Databases were searched until February 2015. Of the 3732 initially identified studies, 5 observational studies with 10,053 patients were analyzed.

RESULTSThe meta-analysis of these studies showed that higher Killips score on admission was associated with higher incidence of NOAF following AMI (odds ratio = 2.29, 95% confidence interval 1.96-2.67, P < 0.00001), while no significant differences exist among individual trials (P = 0.14 and I2 = 43%).

CONCLUSIONSKillips class >I was associated with the higher opportunity of developing NOAF following AMI.

Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.
Algorithms ; Atrial Fibrillation/diagnosis* ; Electrocardiography ; Humans ; Neural Networks, Computer ; Stroke

Atrial Fibrillation/diagnosis* ; Humans ; Quality of Life ; Self Report ; Symptom Assessment

Device based closure of the left atrial appendage (LAA) has emerged as a viable approach for stroke prevention in atrial fibrillation (AF) patients with contraindications to chronic oral anticoagulation. One of the most feared complications is device related thrombus formation. We present a 66-year-old male with chronic AF who developed a life-threatening intracranial bleed on oral anti-coagulation. He subsequently underwent LAA closure using an Amplatzer muscular ventricular septal defect closure device for stroke prevention. However, he was found to have a large thrombus attached to the device a year later. We present a review of the various LAA closure devices, importance of periodic surveillance via echocardiography and management options to prevent this complication. Also, the case highlights the importance of contrast-enhance echocardiography in diagnosis of LAA closure device thrombus.
Aged ; Atrial Appendage ; Atrial Fibrillation ; Diagnosis ; Echocardiography ; Heart Septal Defects, Ventricular ; Heart* ; Humans ; Male ; Stroke ; Thrombosis

Electrocardiosignal (ECG) is an important evidence base for the diagnoses of cardiovascular diseases as well as for the researches in basic and clinical medicine. Because it presents typical non-steady signals, the wave in time can not reflect the essential characteristic very well. In this article, by analysis in time-frequency domain, we have obtained some typical time-frequency diagrams of normal ECG and abnormal ECG, which reflect the comparatively essential characteristics of the original signal in some aspects and seem likely to become a basis for validating the relevant diagnosis.
Algorithms ; Atrial Fibrillation ; diagnosis ; Computer Simulation ; Electrocardiography ; Heart Failure ; diagnosis ; Humans ; Signal Processing, Computer-Assisted