Real-time Atrial Fibrillation Detection Using Artificial Neural Network on a Wearable Electrocardiogram

Providing equal healthcare quality on heart diseases are an issue in developing countries, especially in Indonesia, due to is wide-spread areas. It is founded that the heart diseases occur not only in big cities but also in rural areas, that is caused by unhealthy lifestyle and foods. Heart disease itself is a disease with gradually symptoms changes that can be seen based on the hearts' electrical activity or electrocardiogram signals. Now, wearable medical devices are capable to be worn daily, so that, it can monitor our heart condition and alert if there is an abnormality. An embedded device worn on the chest can be used to perform a real-time data acquisition and processing of the electrocardiogram, that consists of a 1-lead ECG, an ARM processor, a Bluetooth module, an SD card, and rechargeable batteries. Also, by performing a digital filter and Tompkins algorithm, we obtain the P-wave presences and the heart rate variability values (heartbeat, average heartbeat, standard deviation, and root mean square) then by using an artificial neural network with 4 input, 6 hidden, and 1 output layers that has multi-layer perceptrons and backpropagation. We are able to perform a pre-diagnosis of atrial fibrillation, that is one of the common arrhythmias, from 41 recorded training samples (Physionet MIT/BIH AFDB and NSRDB) and 6 healthy subjects as test samples. The neural network has 0.1% error rate and needed 31548 epochs to train itself for classification the heart disease. Based on the results, this prototype can be used as a medical-grade wearable device thatcan help cardiologist in giving an early warning on the user's heart condition, so that it can prevent sudden death due to heart diseases in rural areas.

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Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

Mathematical Problems in Engineering ◽

10.1155/2020/9159158 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Fengying Ma ◽

Jingyao Zhang ◽

Wei Liang ◽

Jingyu Xue

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Artificial Neural Network ◽

Real Time ◽

Classification Model ◽

Ecg Monitoring ◽

Rr Intervals ◽

Ecg Signals ◽

Artificial Neural

Atrial fibrillation (AF), as one of the most common arrhythmia diseases in clinic, is a malignant threat to human health. However, AF is difficult to monitor in real time due to its intermittent nature. Wearable electrocardiogram (ECG) monitoring equipment has flourished in the context of telemedicine due to its real-time monitoring and simple operation in recent years, providing new ideas and methods for the detection of AF. In this paper, we propose a low computational cost classification model for robust detection of AF episodes in ECG signals, using RR intervals of the ECG signals and feeding them into artificial neural network (ANN) for classification, to compensate the defect of the computational complexity in traditional wearable ECG monitoring devices. In addition, we compared our proposed classifier with other popular classifiers. The model was trained and tested on the AF Termination Challenge Database and MIT-BIH Arrhythmia Database. Experimental results achieve the highest sensitivity of 99.3%, specificity of 97.4%, and accuracy of 98.3%, outperforming most of the others in the recent literature. Accordingly, we observe that ANN using RR intervals as an input feature can be a suitable candidate for automatic classification of AF.

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