Development of a new QT algorithm with heterogenous ECG databases

Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

This paper describes the automatic extraction of the P, Q, R, S and T waves of electrocardiographic recordings (ECGs), through the combined use of a new machine-learning algorithm termed generalized orthogonal forward regression (GOFR) and of a specific parameterized function termed Gaussian mesa function (GMF). GOFR breaks up the heartbeat signal into Gaussian mesa functions, in such a way that each wave is modeled by a single GMF; the model thus generated is easily interpretable by the physician. GOFR is an essential ingredient in a global procedure that locates the R wave after some simple pre-processing, extracts the characteristic shape of each heart beat, assigns P, Q, R, S and T labels through automatic classification, discriminates normal beats (NB) from abnormal beats (AB), and extracts features for diagnosis. The efficiency of the detection of the QRS complex, and of the discrimination of NB from AB, is assessed on the MIT and AHA databases; the labeling of the P and T wave is validated on the QTDB database.

Automated and manufacturer independent assessment of the battery status of implanted cardiac pacemakers by electrocardiogram analysis

According to international standards, cardiac pacemakers have to indicate the status of their batteries upon magnet application by specific stimulation patterns. The purpose of this study has been to assess whether this concept can be used as a basis for automated and manufacturer independent examination of the depletion level of pacemakers in the framework of a collaborative telemedical pacemaker follow-up system. A prototype of such a system was developed and tested in a real clinical environment. Electrocardiograms (ECGs) were recorded during magnet application and automatically processed to extract the specific stimulation patterns. The results were used to assign each signal a corresponding pacemaker status: "ok," "replace" or "undefined," based on the expected behavior of the devices as specified by the manufacturer. The outcome of this procedure was compared to the result of an expert examination, resulting in a positive predictive value of 100% for the detection of ECGs indicating pacemaker status "ok." The method can, therefore, be utilized to quickly, safely and manufacturer neutrally classify cases into the categories "ok" and "needs further checking," which - in a telemedical setting - may be used to increase the efficiency of pacemaker follow-up procedures in the future.