An ECG classification using DNN classifier with modified pigeon inspired optimizer

Arrhythmia is a form of heart disease in which the regularity of the pulse is changed.ECG data may be analyzed to detect heart-related illnesses or arrhythmias. This paper presents a wrapper feature selection strategy that employs a Pigeon-inspired optimizer(PIO). The modified Pigeon Inspired Optimizer (MPIO) is used to optimize ECG features and the Deep Neural Network (DNN) to classify the ECG signals. In MPIO, the new blood pigeons were introduced to improve the accuracy of the algorithm. Morphological features, wavelet transform coefficients, and R-R interval dynamic features are extracted for classification of ECG signals. After feature extraction, MPIO is used for feature optimization because optimizing the feature plays a key role in developing the model of machine learning, and irrelevant data features degrade model accuracy and enhance model training time. Using optimised features, the DNN classifier is utilised to classify ECG data. The proposed method achieves 99.10% accuracy, 98.90% specificity, and 98.50% sensitivity. Additionally, when compared with other state-of-the-art methodologies, our method of feature selection also exhibited better outcomes.