An Enhanced Automatic Lung Disease Diagnosis Scheme Using ECG Signals with Integrated Feature Extraction and Improved Deep Learning

An early detection of lung disease can avoid patient death by giving useful treatment. The human with related lung conditions nearly contains related electrocardiogram (ECG) signals. The ECG examination can be an analytical system employed on the screen for various lung diseases. Arrhythmias are discovered through patterns of ECG signals. Nowadays, most of the ECG analysis is done according to the medical team‟s personal opinion, which may have led to more burden. Therefore, in this paper, an automatic lung disease diagnosis scheme is presented through an accurate ECG signal categorization using improved deep learning processes. Initially, ECG signal data is pre-processed with noise removal and QRS complex discovery schemes. Subsequently, an integrated feature extraction method is proposed in this paper to extract the ECG wave features. The presented automatic lung disease detection scheme is examined using the ECG signals dataset collected from a MIT-BIH arrhythmia database.