Arrhythmias are disturbances in the electrical activity of the heart that can lead to a wide range of clinical manifestations, from benign symptoms to life-threatening events. This study presents a methodology for the early detection and classification of arrhythmia based on the analysis of 2-second ECG fragments derived from long-duration recordings. The data source is the ECG Fragment Database for the Exploration of Dangerous Arrhythmia from PhysioNet, which includes multi-minute ECG recordings segmented into short, high-resolution fragments. The database categorizes records into six arrhythmia classes to identify the presence of life-threatening ventricular fibrillation (class 1) and to identify warning signs such as high-frequency ventricular tachycardia (class 3) and torsade de pointes ventricular tachycardia (class 2). Classes 4, 5, and 6 represent another group of dangerous arrhythmias. The proposed pipeline begins with a signal preprocessing stage, where a two-step filter is applied. The first step uses Approximate Entropy (ApEn) as a filter to quantify signal regularity and detect high-noise segments. Segments with entropy values above a defined threshold are corrected using polynomial interpolation. In the second step, a Normalized Least Mean Squares (NLMS) adaptive filter refines the signals using a reference channel. Subsequently, the Permutation Entropy (PE) of each 2-second segment is computed in order to extract features that capture the nonlinear and temporal complexity of the ECG signal. These features are then utilized to train a deep neural network classifier, which is optimized for distinguishing between various classes of arrhythmia. To enhance the model's generalization capabilities and address class imbalance, an unsupervised clustering approach using k-means is employed, followed by a genetic algorithm-driven data augmentation strategy. This hybrid model, combining entropy-based feature extraction, with accuracy values exceeding 90%. The system has been designed to contribute to early diagnostic workflows by providing rapid and reliable identification of high-risk ECG patterns