Comparing the consistency of electrocardiogram interval measurements by resting ECG versus 12-lead Holter

Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection

Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.

An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm

With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.

Comparing the consistency of electrocardiogram interval measurements by resting ECG versus 12-lead Holter

In clinical trials, traditionally only a limited number of 12-lead resting electrocardiograms (ECGs) can be recorded and, thus, long intervals may elapse between assessment timepoints and valuable information may be missed during times when patients' cardiac electrical activity is not being monitored. These limitations have led to the increasing use of Holter recorders which provide continuous data registrations while reducing the burden on patients and freeing up time for clinical trial staff to perform other tasks. However, there is a shortage of data comparing the two approaches. In this study, data from a randomized, double-blind, four-period, crossover thorough QT study in 40 healthy subjects were used to compare continuous 12-lead Holter recordings to standard 12-lead resting ECGs which were recorded in parallel. Heart rate and QT interval data were estimated by averaging three consecutive heartbeats. Values exceeding the sample average by more than 5% were tagged as outliers and excluded from the analysis. Visual comparisons of the ECG waveforms of the Holter signal showed a good correlation with resting ECGs at matching timepoints. Resting ECG data revealed sex differences that Holter data did not show. Specifically, women were found to have a longer QTcF of 20 ms, while men had a lower heart rate. We found that continuous recordings provided a more accurate reflection of changes in cardiac electrical activity over 24 hr. However, manual adjudication is still required to ensure the quality and accuracy of ECG data, and that only artifacts are removed thereby avoiding loss of true signals.