Optimization and Prioritization of Cardiac Messages

Optimization and Prioritization of Cardiac Messages

Piotr Augustyniak (AGH University of Science and Technology, Poland) and Ryszard Tadeusiewicz (AGH University of Science and Technology, Poland)
Copyright: © 2009 |Pages: 11
DOI: 10.4018/978-1-60566-080-6.ch010
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Abstract

This chapter presents a proposal of medically justified modulations of the frequency of cardiac reporting, implemented in a client-server distant cooperation model. The supervising center analyzes incoming messages and other conditions, and issues the desired reporting interval back to the remote device. As a result of the authors’ tests and simulations, this method may reduce wireless channel usage and increase remote autonomy up to three times.
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Variability Analysis Of Most Common Diagnostic Parameters In Ecgs

The electrocardiographic signal is the carrier of all diagnostic parameters derived from it, and the need for high sampling frequency is justified by expectation of high-fidelity digital representation of analog signals. Within the ventricle contraction period represented by the QRS complex, the signal frequency is occasionally high, but for the rest of the signal there is a considerable redundancy of adjacent samples (Bailey et al., 1990; Augustyniak, 2002). This redundancy was the key point of the family of algorithms for the ECG compression based on a temporal similarity of samples. Although it is theoretically possible, there is no diagnostic parameter showing variability as high as the raw representation of a cardiac electrical field.

Although changes in electrical heart activity are continuous, they are rarely observed and reported within a heartbeat. The only exception is probably a baseline and noise level estimation that should reflect abrupt changes in noise power and thus alter the ECG measurement conditions. These measurements are used in the compensation or improvement of the signal within short strips (a few samples), however their local values are rarely stored in the internal database and included in the report. In most cases the signal quality estimate and the baseline level are stored once per heartbeat. The first value is used to assess the reliability of heartbeat-derived diagnostic parameters or optimal choices of geometrical aspects in the case of multilead recordings. The second is used for baseline compensation in wave borders and electrical axes determination procedures.

As stated previously, the highest variability of diagnostic parameters is limited to the frequency corresponding to a beat-to-beat rate. Nevertheless, multiple parameters are computed at that rate, but due to known physiological limitations, high variability is assumed to be caused by external sources (e.g., noise or measurement uncertainty) and eliminated. The reported value is a result of averaging the adjacent beat-derived components over a specified period of time or over a given number of heartbeats. Arrhythmia sequences also belong to that range of variability, since they cannot be detected more precisely than with the accuracy of a few heartbeats. The other group of parameters shows the variability reflecting slow changes in conduction, re-polarization conditions like those influenced by chemical or hormonal signals.

The general classification of parameter variability includes three groups:

  • 1.

    Parameters of High Variability (0.8-3Hz)—RR-interval, wave axes, PQ-interval, baseline level and noise, T wave alternans, respiration-induced changes, intermittent morphology changes.

  • 2.

    Parameters of Medium Variability (0.03-1.5Hz)—Dominant rhythm, heart rate, arrhythmia.

  • 3.

    Parameters of Low Variability (0.00067-0.03Hz)—ST-level and slope changes, QT-interval, block and infarct changes, drug-induced morphology changes.

The diagnostic parameters variability within the confines specified above shows a significant degree of dependency on patient status. As a general rule, good or stable patient status implies a lower probability of change and therefore the necessary frequency of update for a majority of parameters. Taking the normal sinus rhythm (NSR) as an example, to fulfill the medical definition, four parameters must meet specified criteria: P wave existence and uniqueness, PQ intervals within the range of 60-220ms, PQ interval stability, and P wave axis stability. Considering all physiological conditions, once the NSR is detected and confirmed, the sudden occurrence of similar non-NSR rhythm not satisfying a single of these criteria is very improbable. This presents the opportunity to limit the criteria under consideration to two alternately selected from within the four.

Since the investigation of proper reporting contents and frequency dependent on patient condition is still not concluded, we had to report some preliminary results together with assumptions on the remaining outcomes specifying the reporting frequency separately for each ECG parameter (Table 1).

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