Design and Preliminary Results of Sensing and Detection for an Extravascular Implantable Cardioverter-Defibrillator.

OBJECTIVES: This study reports the sensing and arrhythmia detection performance of a novel extravascular (EV) implantable cardioverter-defibrillator (ICD) in a first-in-human pilot study. BACKGROUND: The EV ICD lead is implanted in the substernal space, resulting in novel sensing and detection challenges. It uses a programmable sensing profile with new or modified discrimination of oversensing and of ventricular tachycardia (VT) from supraventricular tachycardia (SVT). METHODS: Electrograms were post-processed from induced ventricular fibrillation (VF) at implant to determine virtual detection times for each programmable sensitivity and the least-sensitive safe sensitivity setting. In ambulatory patients, programmed sensitivity provided at least a twofold safety margin for detecting induced VF. Noise discrimination was stress tested, and the effects of source, posture, and lead maturation were determined on electrogram amplitude. Telemetry Holter monitors were used to quantify undersensing and oversensing. RESULTS: In 20 patients at implant, the least-sensitive safe sensitivity for VF detection ranged from 0.1 to 0.6 mV. Seventeen patients were followed up for a total of 16.6 patient-years. Electrogram amplitudes were stable over time, but there were significant differences among postures and sensing vectors. For the primary sensing vector, the weighted oversensing and undersensing rates were 1.03% and 0.40% respectively, on a beat-to-beat basis. Oversensing did not cause inappropriate therapy in patients with in situ leads. Oversensing discriminators withheld VF detection in 4 of 5 spontaneous, sustained oversensed episodes. SVT-VT discriminators correctly classified 93% of 128 sustained SVTs in monitor zones. CONCLUSIONS: In the EV ICD pilot study, oversensing did not cause inappropriate therapy during ambulatory follow-up of stable leads.

Reconstruction of an 8-lead surface ECG from two subcutaneous ICD vectors.

INTRODUCTION: Techniques exist which allow surface ECGs to be reconstructed from reduced lead sets. We aimed to reconstruct an 8-lead ECG from two independent S-ICD sensing electrodes vectors as proof of this principle. METHODS: Participants with ICDs (N=61) underwent 3minute ECGs using a TMSi Porti7 multi-channel signal recorder (TMS international, The Netherlands) with electrodes in the standard S-ICD and 12-lead positions. Participants were randomised to either a training (N=31) or validation (N=30) group. The transformation used was a linear combination of the 2 independent S-ICD vectors to each of the 8 independent leads of the 12-lead ECG, with coefficients selected that minimized the root mean square error (RMSE) between recorded and derived ECGs when applied to the training group. The transformation was then applied to the validation group and agreement between the recorded and derived lead pairs was measured by Pearson correlation coefficient (r) and normalised RMSE (NRMSE). RESULTS: In total, 27 patients with complete data sets were included in the validation set consisting of 57,888 data points from 216 full lead sets. The distribution of the r and NRMSE were skewed. Mean r=0.770 (SE 0.024), median r=0.925. NRMSE mean=0.233 (SE 0.015) median=0.171. CONCLUSIONS: We have demonstrated that the reconstruction of an 8-lead ECG from two S-ICD vectors is possible. If perfected, the ability to generate accurate multi-lead surface ECG data from an S-ICD would potentially allow recording and review of clinical arrhythmias at follow-up.

Electrode positions, transformation coordinates for ECG reconstruction from S-ICD vectors.

The article contains data pertaining to the reconstruction of an 8-lead ECG from 2 subcutaneous implantable cardioverter defibrillator vectors. The location of electrodes on the precordium required for the data collection are detailed; the flow chart for patient selection and exclusion is shown; the summary data of the root mean square error (RMSE) (in microvolts) and Pearson r for the ECG transformation all cases and the pearson correlation for all the leads measured and reconstructed leads are also shown. Detailed background, methodology and discussion can be found in the linked research article.