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M Morando

Publications and source records attributed to M Morando.

9 recordsLinked to original sources

Computationally inexpensive methods for intra-cardiac atrial bipolar electrogram compression.

AIM: This paper reports studies of mathematical algorithms for intra-cardiac atrial bipolar electrogram compression suitable with implementation on implantable devices. PATIENTS AND METHODS: Bipolar intra-cardiac electrograms (IEGMs) of high right atrium were obtained from 20 patients who underwent electrophysiological studies for arrhythmias. Four thousand seven hundred and eighty-two seconds of IEGM were collected and divided into three rhythm groups: sinus rhythm (SR), atrial fibrillation (AF) and atrial flutter (AFL). Since mathematical algorithms suitable for use with implantable devices demand low computational cost, we employed piecemeal linear approximation methods (ZOP--Zero Order Prediction and SAPA--Scan Along Polygonal Approximation), and beat detection method (Peak) both or which need small numbers of operations to perform electrogram compression. Compression ratio (CR) and percent root mean square difference (PRD) were used to compare the three methods, with statistical analyses performed using paired t-test. RESULTS AND CONCLUSION: The best performance was obtained using the Peak method which reaches an average CR of 10.6 in the case of SR group, 2.8 for AF, and 3.6 for AFL groups, respectively, while PRD lies below 2% for SR and AFL groups and 6% for the AF group. Results show that, for bipolar electrograms, the Peak method reaches statistically significant better performance (P<0.001) in all cases except for Peak vs SAPA applied to AF (P=0.2). The number of operations necessary to compress the data indicate that time consumption can be reduced to be suitable for real time compression in implantable devices. The Peak method, which was assumed to receive the instant of occurrence of each recognized beat, from the hardware of the device, requires fewer operations than ZOP and SAPA. Increasing the length of electrograms recorded in pacemakers will enhance the amount of information provided by the implantable device, allowing more detailed characterization of the intra-cardiac activity and leading to new perspectives in arrhythmia diagnosis and therapy.

Adult↗

Automatic atrial tachyarrhythmia detection from intracardiac electrograms.

BACKGROUND: Automatic atrial tachyarrhythmia recognition is crucial in order to allow a correct switching-mode function of dual-chamber pacemakers and to avoid inappropriate shocks of ventricular implantable cardioverter-defibrillators. In this paper we considered three algorithms suitable for implantable devices. The first was based on the atrial cycle length; the others analyze different morphologic characteristics of atrial signals. METHODS: Intracardiac bipolar electrogram recordings were obtained from the high right atrium during electrophysiological study. Twenty patients were considered, some of them presenting with different types of cardiac rhythm at different intervals of the study. Cardiac rhythms were divided into three groups: sinus rhythm consisting of 2,196 s obtained from 12 subjects, atrial fibrillation consisting of 771 s obtained from 7 subjects, and atrial flutter consisting of 1,793 s obtained from 7 subjects. The automatic detection was performed on each electrogram segment lasting 1 or 4 s. Atrial segments were separated into two subgroups: the first for the training of the algorithm and the second for testing and validation of results. We considered two types of statistical analysis: comparison between pairs of rhythm (paired classification), and classification among the three different groups (direct classification). RESULTS: The combination of the cycle length algorithm with a morphological method achieved the best performance for both statistical analyses. Paired classification resulted in the following: atrial fibrillation vs sinus rhythm was detected with no error; atrial flutter vs sinus rhythm with a total accuracy of 99.3% (sensitivity 99.4%, specificity 99.2%); atrial fibrillation vs atrial flutter with a total accuracy of 99.1% (sensitivity 98.5%, specificity 99.4%). The total accuracy achieved for the direct classification was 98.6% (average sensitivity 98.5%, specificity 98.8%). CONCLUSIONS: Our results support the association of algorithms for future enhancement of atrial tachyarrhythmia detection in dual-chamber devices, thanks to the limited computational effort.

Algorithms↗