Key result
The proposed algorithm using mixture Gaussian functions and dynamic programming determined P- and T-peak locations with RMSEs of 0.085 s and 0.091 s, achieving 96.13% sensitivity and 97.70% predictivity.
Why the study?
Detection and tracking of the P- and T-waves in ECG signals require improved methods that incorporate prior knowledge and robustness to QRS detection errors.
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A novel algorithm using mixture Gaussian functions and dynamic programming provides highly accurate P- and T-wave delineation in ECG signals, outperforming existing DEOS and GBI methods.
Detection and tracking of the P- and T-waves are important issues in the analysis and interpretation of the ECG signals. This paper addresses the problem by using two mixture Gaussian function and the Dynamic programming. A key feature of the proposed algorithm is that it allows to incorporate the prior knowledge about the P/T wave location variations and robustness to errors in QRS detection. The proposed algorithm is evaluated on the annotated QT-database and compared against the algorithms based on differential evolution optimization strategy (DEOS) and generating blocks of interest (GBI). The experiments show that the proposed method determines the P- and T-peak locations with a root mean square error of 0.085 s and 0.091 s respectively. Both these values are better than the corresponding values from DEOS and GBI. Similarly, the proposed algorithm achieves a sensitivity of 96.13% and predictivity of 97.70%. While the predictivity is higher than both DEOS and GBI, the sensitivity is on par with GBOI and higher than that of DEOS.
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MV et al. (2019) studied ECG signals. Algorithm using two mixture Gaussian function and Dynamic programming vs. Algorithms based on differential evolution optimization strategy (DEOS) and generating blocks of interest (GBI) was evaluated on P- and T-peak location root mean square error, sensitivity, and predictivity. The proposed algorithm using mixture Gaussian functions and dynamic programming determined P- and T-peak locations with RMSEs of 0.085 s and 0.091 s, achieving 96.13% sensitivity and 97.70% predictivity.
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