This study tackles the ECG classification problem by means of a methodology, which is able
to enhance classification performance while simultaneously reducing the computational …

In this paper, the authors propose an improved convolutional neural network for automatic
arrhythmia classification using Electro-Cardio-Gram (ECG) signal. It is essential to …

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading
to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or …

Electrocardiogram (ECG), a typical non-stationary signal, is used for diagnosis of cardiac
disorders. The electrical activities such as depolarization and re-polarization of heart are …

With creasing computational power, sophisticated algorithms has e hero proposed to
improve the prediction accuracy of ECG waveform classification systems. One such …

The electrocardiogram (ECG) is a non-invasive test that records the electrical activity of the
heart and is important in the investigation of cardiac abnormalities. Each portion of the ECG …

The use of wearable recorders for long-term monitoring of physiological parameters has
increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five …

The body temperature is normally maintained within a narrow range. In a hot environment,
however, heat gain can exceed heat loss and heat related disorders may occur. Heat …

The arrhythmias or abnormal rhythms of the heart are common cardiac riots and may cause
serious risks to the life of people, being one of the main causes on deaths. These deaths …

ECG plays an important role in cardiac disease diagnosis. Classification of this cardiac
signal using machine learning techniques will be a supportive tool for the physicians …