  The state-of-the-art cardiovascular disease diagnosis techniques use
machine-learning algorithms based on feature extraction and classification. In
this work, in contrast to a conventional single Electrocardiogram (ECG) lead,
two leads are used, and autoregressive (AR) coefficients and statistical
parameters are extracted to be used as features. Four machine-learning
classifiers support-vector-machine (SVM), K-nearest neighbors (KNN),
multi-layer perceptron (MLP), and Naive Bayes are applied on these features to
test the accuracy of each classifier. For simulation, data is collected from
the MIT-BIH and Shaoxing Peoples Hospital China (SPHC) database. To test the
generalization ability of our proposed methodology machine-learning model is
built on the SPHC database and tested on the MIT-BIH database and
self-collected datasets. In the single-database simulation, the MLP performs
better than the other three classifiers. While in the cross-database
simulation, the SVM-based model trained by the SPHC database shows superiority.
For normal and LBBB heartbeats, the predicted recall respectively reaches 100%
and 98.4%. Simulation results show that the performance of our proposed
methodology is better than the state-of-the-art techniques for the same
database. While for cross-database simulation, the results are promising too.
Finally, in the demonstration of our realized system, all heartbeats collected
from healthy people are classified as normal beats.
