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

Computer analysis of physiologic signals in a cardiovascular research laboratory

A comprehensive computer program which provides immediate computation and feedback has been developed for data acquisition and analysis of signals in a cardiovascular animal laboratory. The system is based on a microcomputer equipped with analog-to-digital converter and supports function modules which digitize, filter, and differentiate up to 8 simultaneously sampled cardiovascular signals. The program detects, analyses, and plots incoming and averaged beats. Beat-by-beat signal averaging for each channel is performed and cardiac cycles are partitioned automatically. For each cardiac and average cycle the amplitude at 6 physiologic fiducial markers are measured and derived calculations are made. Channel vs channel plots and loop area measurements are also computed and displayed. The computer algorithms have been shown to give accurate, precise, and reproducible results when tested on canine cardiovascular data. Also, it has been demonstrated that signal averaging is an appropriate analysis technique for cardiovascular signals.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29539/1/0000627.pd

Computer modelling and interpretation of paced electrocardiograms for analysis of pacemaker function.

Electrocardiographic (ECG) analysis is critical to pacemaker patient follow-up for identifying pacemaker function. Because of the complexity of manual diagnosis of the paced ECG, sophisticated computer analysis is proposed to aid the interpretation of pacemaker malfunction. An automated ECG analysis algorithm for dual-chamber pacemakers has been developed using pacing stimulus onset, atrial and ventricular depolarization onsets, intervals, and morphologies. The interpretation algorithm defines ECG cycles and pacemaker operating mode for each cycle. Appropriate analysis of atrial and ventricular output, capture, and sensing is performed and specific interpretations given. The algorithm is unique in the utilization of intrinsic pacemaker logic, atrial depolarization timing, and waveform morphology in the decision process. Pacemaker blanking, refractory, and other periods are incorporated into the analysis as well as tolerances in device activities and cardiac chamber activation. Software is implemented as a rule-based structure which includes a pacemaker description language that allows a variety of pacemaker model analyses to be incorporated with minimal effort. A new heart-pacemaker interaction (HPI) model was developed to serve as a development tool for the interpretation algorithm. The stochastic network model provides a concise framework for simulation of 25 classes of arrhythmias, 13 pacemaker modes, and a unique feature of simulation of a variety of pacing and sensing failures. Seventy-seven simulated paced ECG passages served as the system training set. The computer algorithm was tested by varying pacemaker parameters to simulate failures in five patients with DDD (dual-chamber pacing and sensing) pacemakers in which surface and esophageal ECGs were recorded. Thirty-three 15 second ECG passages were randomly selected which included single or multiple pacemaker failure simulations of output, capture, and sensing in atrial and ventricular chambers. Two errors in automated analysis were detected which yielded an overall computer algorithm success of 99%. This thesis presents a robust computer algorithm for analysis of pacemaker ECGs for the determination of pacemaker function and malfunction. The system was validated using a novel model of heart-pacemaker interaction and clinical ECGs.Ph.D.BioengineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/105373/1/9124014.pdfDescription of 9124014.pdf : Restricted to UM users only

Computer simulation of heart-pacemaker interaction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28823/1/0000657.pd

An algorithm for the quantification of ST-T segment variability

A template boundary algorithm which quantitatively determines repolarization (ST-T segment) variability in a normal population has been developed. The algorithm defines an initial ST-T template for comparison with successive beats. Variability is quantified using boundary limits around the template which are widened, when necessary, to included incoming ST-T segments. The boundaries at the end of each hour are stored and the collection of boundaries over a set of normal subjects quantifies the normal variation over the entire ST-T segment. The algorithm can be used to determine prospectively normal ST-T variability based on a regression analysis of R-wave or T-wave amplitude, and QT interval. Application of these boundary predictions should be useful in distinguishing repolarization changes secondary to ischemia from normal variability.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27829/1/0000235.pd