Fig. 1 | Scientific Reports

Fig. 1

From: Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks

Fig. 1

Block diagram of the multi-class heart disease classification model by utilizing Noise Filtering (NF), Moving Average Filter (MAF), Pan Tompkins Algorithm (PTA), Algebraic Integer quantized Stationary Wavelet Transform (AI-SWT), Low-rank Kernelized Density-Based Spatial Clustering of Applications with Noise (LK-DBSCAN), Heming Wayed Polar Bear Optimization (HeWaPBO), and C squared Pool Sign BI-power-activated Deep Convolutional Neural Network (CP-SBI-DCNN) techniques.

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