Arrhythmia Evaluation in Wearable ECG Devices

Abstract

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

“…Works in short-term monitoring (duration of some minutes) and new public databases [ 50 ] can be found, as the intelligent heart-monitoring public cryptosystem, which involves a patient-worn ECG sensor and a remote monitoring station, using PT and classification trees as heartbeat detection and classification algorithms, respectively. More advanced methods, such as sample entropy, fast Fourier transform, and ANN, were recently selected for the integrated detection algorithms [ 21 ], in order to validate an arrhythmia detection method for supra and ventricular ectopic beats and atrial/ventricular fibrillation, when using the ANSI/AAMI EC57:2012 standard. Lossy methods, which traditionally were avoided in this setting, have been successfully revisited [ 18 ], and different compression methods based on the compression ratio and percentage root-mean-squared difference were proposed and advantageously benchmarked, including in terms of the accuracy of R-wave detection.…”

“…Noise data were made using physically active volunteers with standard ECG recorders, leads, and electrodes, and then adding it to the recordings from the MIT–BIH [ 19 , 20 ]. We selected these data for the wide use of its noise components (e.g., see [ 21 ]). LTSTDB.…”

A Solution for Improvement of Ecg Arrhythmia Recognition Using Respiration Information

“…Experimental results in the MIT-BIH database and in the actual patient showed that the EMD method has little effects only on the ECG characteristic features. The EMD method was also used in [22] in combination with the discrete wavelet transform (DWT) to detect R-peaks and QRS complex. The Probabilistic neural network (PNN) and radial basis function neural network (RBF-NN) were used to recognize the arrhythmia beats.…”

Computational Fluid Dynamics Based Fuzzy Control Optimization of Heat Exchanger via Genetic Algorithm

“…Artificial intelligence (AI) has been widely administered in considerable applications. It has been utilized for medical use such as arrhythmia problems [1,2], anesthesia [3,4] and blood pressure estimation [5]. Moreover, the AI also has been applied to energy systems [6][7][8], electromagnetic field [9] and shape optimization to increase the aerodynamics of the unmanned aerial vehicle [10].…”