Search

In the context of increase in cardiovascular diseases in the aging population, including a high prevalence of atrial fibrillation (AF), the development of medical devices to ensure patient follow-up is of major interest. The purpose of this study is to assess the ECG signal quality of a one-lead in a new miniaturized device on healthy volunteers submitted to several conditions reflecting daily life activity. Our results show that the P wave identification is not enough reliable to consider the detection of its potential disappearance in case of AF. However, we show that the ECG signals can be used to robustly detect the RR intervals. To conclude, for rhythm disturbance detection, an automatic and specific analysis of RR variability has now to be integrated in this new multimodal device. Clinical relevance - This study confirms the potential interest of implantable medical device in the management of cardiac arrhythmias notably in the context of the individual follow up of aging patients.

In the context of monitoring patients with heart failure conditions, the automated assessment of heart sound quality is of major importance to insure the relevance of the medical analysis of the heart sound data. We propose in this study a technique of quality classification based on the selection of a small set of representative features. The first features are chosen to characterize whether the periodicity, complexity or statistical nature of the heart sound recordings. After segmentation process, the latter features are probing the detectability of the heart sounds in cardiac cycles. Our method is applied on a novel subcutaneous medical implant that combines ECG and accelerometric-based heart sound measurements. The actual prototype is in pre-clinical phase and has been implanted on 4 pigs, which anatomy and activity constitute a challenging environment for obtaining clean heart sounds. As reference quality labeling, we have performed a three-class manual annotation of each recording, qualified as "good", "unsure" and "bad". Our method allows to retrieve good quality heart sounds with a sensitivity and an accuracy of 82% ± 2% and 88% ± 6% respectively. Clinical Relevance- By accurately recovering high quality heart sound sequences, our method will enable to monitor reliable physiological indicators of heart failure complications such as decompensation.