Automated detection of heart defects in athletes based on electrocardiography and artificial neural network

• Main Authors: Emmanuel Adetiba, Veronica C. Iweanya, Segun I. Popoola, Joy N. Adetiba, Carlo Menon
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-01-01
• Series: Cogent Engineering
• Subjects: artificial neural network; hypertrophic cardiomyopathy; sudden cardiac death; tachyarrhythmia; bradyarrhythmia; electrocardiography; multilayer perceptron,
• Online Access: http://dx.doi.org/10.1080/23311916.2017.1411220

Electrocardiography (ECG) has proven to be one of the most efficient ways of tracking heart defects in athletes. However, the interpretation of electrocardiograms often require the expertise of a cardiologist. Meanwhile, an automated heart monitoring system could be used to ensure early heart defect detection in athletes, even in the absence of a cardiologist. In this paper, an automated heart defect detection model is proposed for athletes using ECG and Artificial Neural Network (ANN). We developed an ECG biomedical equipment to acquire 400 ECG data vectors from 40 participants, who comprises of athletes and non-athletes. Four classes of possible heart conditions among athletes, namely: normal, tachyarrhythmia, bradyarrhythmia and hypertrophic cardiomyopathy were considered. The ECG data collected were pre-processed and features were extracted based on first order moment. Different ANNs were trained to correctly classify the ECG data. By and large, the performances of ANNs that were trained based on Levenberg-Marquardt learning algorithm outperformed those trained based on Scale Conjugate Gradient learning algorithm. The network architecture with tansig activation function at both hidden and output layers and ten neurons in the hidden layer (TTLM) produced the best performance that cut across all the key performance indicators. The generalization testing of the developed TTLM model with new input data (that were excluded from the training dataset) produced acceptable results with classification accuracy, sensitivity and specificity of 90.00, 91.96 and 97.06% respectively. In essence, the implementation of the developed model in this study could potentially assist in reducing sudden cardiac death among athletes.