Immediately after implant, the GUARDIAN is set to learn what is “normal” for each patient by analysing thousands of heart signals over a 7-to-14-day period. The result is a baseline electrogram segment for the patient. Once the initial baseline is in determined, the GUARDIAN continuously collects a 10-second electrogram every 90 seconds, analyses it, and compares the result to the baseline segment. The baseline segment is continuously assessed and is updated every hour.
An Emergency Alarm will sound when 3 consecutive segments are significantly different from the baseline segment.
No other device in the world is approved to detect the onset of ACS events. Devices without leads (such as loop recorders) are not capable of reliably detecting the subtle changes to the ST segment that is required for ACS event detection. Those devices are limited to detecting changes related to heart rate and heart rhythm anomalies such as atrial fibrillation, syncope, etc. The GUARDIAN can detect all those conditions as well as the most life-threatening condition - myocardial infarction.
The GUARDIAN implant procedure is identical to that of the very well-known and understood single chamber pacemaker implant procedure. It is an outpatient procedure taking approximately 30-minutes under local anaesthesia.
To detect ischemia throughout the entire heart, the sensing electrode is placed in a central location. For the GUARDIAN the recommended position for the placement of the electrode tip is in the RV apex (or similar). As part of the implant procedure the electrode signal is checked and adjustments are made to the position if necessary.
The sophisticated software algorithms in the GUARDIAN have a number of features designed to minimise false positive detections. One of the primary methods involves the development of a “self-normative baseline” for each patient via machine learning (AI) analysis. The GUARDIAN continuously compares the latest 10-second ECG strip to a constantly refreshed composite ECG that represents what is “normal” for that specific patient, i.e. the “self-normative baseline”. The GUARDIAN measures the acute change from normal and alarms only when this acute change exceeds the thresholds set for that specific patient for a specific length of time. This approach dramatically reduces the false positive emergency department visits. Machine learning (AI) techniques are used in both the ST detection algorithm and the algorithm in the device programmer that determines optimal thresholds for detection.