We will develop a chronically implanted heart monitor that detects acute ischemia by analyzing electrocardiographic (ECG) waveforms recorded by subcutaneous electrodes. The two specific aims of this Phase I research are: (1) to further develop our ischemia detection algorithm by refining heart-rate based adjustment of ECG feature thresholds and optimizing temporal denoising and discrimination of ECG data and extracted features;and (2) to evaluate our refined ischemia detection algorithm by comparing the extent of ECG feature changes (e.g. ST segment changes) during intervals of induced acute ischemia with changes that occur during normal daily life. The latter aim will be achieved by recruiting 36 patients and recording two datasets for each patient. The first dataset will consist of Holter recordings from three candidate bipolar leads for 1-day before patients undergo balloon angioplasty. The second dataset will consist of Holter recordings that will be obtained from these patients during balloon angioplasty. The results of the research will be a refined and validated ischemia detection algorithm and the selection of two leads from our three candidate leads. Phase II of the project will entail creating a prototype device based on this algorithm and the chosen leads.
Heart attacks are the leading cause of death in the United States. Early detection of heart attacks by an implantable heart monitor will help to save lives and improve the health of survivors.
| Hopenfeld, Bruce; John, M Sasha; Fischell, Tim A et al. (2012) A statistically based acute ischemia detection algorithm suitable for an implantable device. Ann Biomed Eng 40:2627-38 |
