Application of the Hidden Markov Model for determining PQRST complexes in electrocardiograms
This work addresses ECG signal segmentation for medical diagnostics, but it is incremental as it builds on existing methods like the hidden Markov model and Pan-Tompkins algorithm.
The authors tackled the problem of segmenting PQRST complexes in electrocardiograms by applying a hidden Markov model with various parameters, achieving results comparable to a modified Pan-Tompkins algorithm for QRS duration detection.
The application of the hidden Markov model with various parameters in the segmentation task of QRS, ST, T, P, PQ, ISO complexes of electrocardiograms is considered. Models were trained using the Viterbi algorithm using the QT Database. For comparison, the Pan-Tompkins algorithm for searching for the duration of QRS complexes was modified.