Key result
Pacemaker telemonitoring algorithm achieves ~98% accuracy compared to specialist manual review.
Why the study?
A telemonitoring algorithm for pacemaker working status was developed to extend home ECG and blood pressure monitoring for pacemaker users.
Does a specially designed pacemaker working status analysis algorithm accurately recognize pacemaker function compared to manual classification by specialists?
Does a specially designed pacemaker working status analysis algorithm accurately recognize pacemaker function compared to manual classification by specialists?
A newly developed telemonitoring algorithm for pacemaker users demonstrated high accuracy (98.4%) in recognizing pacemaker working status.
Supports feasibility of automated pacemaker telemonitoring; leaves open prospective clinical validation before practice adoption.
To extend the application of a previously developed home ECG and blood pressure telemonitoring system for pacemaker users, a specially designed pacemaker working status analysis algorithm is developed in this paper. This paper includes four sections. First, a pacing beat and spontaneous beat recognition algorithm are established in which a FIR filter and three threshold methods are used to pick up the pacing spikes and a dynamic threshold modification method is proposed for error elimination. Second, a beat classification algorithm is developed based on decision rules for six commonly used pacemaker types. Third, a pacemaker malfunction recognition algorithm is established to evaluate the pacemaker working status. Fourth, the statistics of the analysis and the report are also provided. To validate the algorithm, clinical data were collected and manually classified by specialists. Then, the classification and recognition results were obtained with the clinical data. Results show that the algorithm can achieve a recognition rate of 98.4%. Results also indicate that the algorithm is capable of working in real-time speed. The algorithm has been incorporated into the telemonitoring system, and trial application cases are also reported.
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Bai et al. (1999) studied Pacemaker users. Pacemaker working status telemonitoring algorithm vs. Manual classification by specialists was evaluated on Recognition rate. A pacemaker working status telemonitoring algorithm achieved a recognition rate of 98.4% when validated against manual classification by specialists.
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