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Adaptive Hermite models for ECG data compression: performance and evaluation with automatic wave detection

Abstract:

An orthogonal transformation based on Hermite functions is proposed as a method for ECG data compression. In order to apply the procedure four signal windows are selected in each beat, corresponding to the principal ECG features: P wave, QRS complex, ST segment and T wave. The performance of the method is analysed calculating the compression ratio (CR) and the relative mean-square error (MSE) in each window and in the whole beat. The method has been applied to ECG records from MIT/BIH arrhythmia database. In normal beats with a CR=11.6, we have obtained a MSE = (0.09 ± 0.02)%. In ECG signals containing normal beats and multiform PVCs a MSE = (0.56 ± 3.41)% is obtained, with a CR=10.3. To analyse the clinical applicability of the method, the algorithm was evaluated with an automatic wave detection program. Differences between the automatic measures in the original signal and in the reconstructed signal were compared, and shown a good agreement.