Fig 1.
Multi-objective optimization leading to s Pareto-front of all solutions as the main objective.
Fig 2.
Determination of sinusoidal frequency and phase content of local sections of ECG signal versus time based on (A) short-time-Fourier-transformation; and (B) wavelet-transformation.
Fig 3.
Flowchart of multi-objective optimization neural network for reconstruction of ECG data.
Fig 4.
Neural network connections with the input, hidden and output layers of nodes representing a connection from a neural output to the input of a neuron.
Fig 5.
Reconstructed signal output after ECG compression for a longer period of 6000 seconds.
Fig 6.
Comparison of waveform by the hidden neuron and input data models using a match distance approach.
Fig 7.
Reconstructed ECG signal waveform based on the wavelet compression and neural network methods in comparison with the original signal.
Fig 8.
Average percentage root-mean-squared difference (PRD) results based on different ECG data compression ratios using transform and neural network approaches (A); Average encoding time versus ECG compression ratio using transform and neural network approaches (B).