Acute effects of sublingual nitroglycerin on cardiovagal and sympathetic baroreflex sensitivity

The effects of nitroglycerin (glyceryl trinitrate, GTN) on baroreflex sensitivity (BRS) are incompletely understood. Moreover, there are no reports evaluating the acute responses in both the sympathetic BRS (SBRS) and the cardiovagal BRS (CBRS) to the administration of sublingual GTN. We hypothesized that sublingual GTN modulates both CBRS and SBRS. In 10 healthy subjects, beat-to-beat heart rate (HR), blood pressure (BP) and muscle sympathetic nerve activity (MSNA) were recorded before and for 10 min after sublingual administration of GTN 0.4 mg. SBRS was evaluated from the relationship between spontaneous variations in diastolic BP and MSNA. CBRS was assessed with the sequence technique. These variables were assessed during baseline, during min 3rd - 6th (Post A) and 7th -10th min (Post B) after GTN administration. Two min after GTN administration, MSNA increased significantly and remained significantly elevated during recording. Compared to baseline, CBRS decreased significantly (Post A: 12.9 ± 1.6 to 7.1 ± 1.0 ms/mmHg, P < 0.05), while SBRS increased significantly (Post A: 0.8 ± 0.2 to 1.5 ± 0.2 units･beat-1･mmHg-1, P < 0.05) with an upward shift of the operating point. There were no differences in these variables between Post A and B. A clinical dose of GTN increased MSNA rapidly through effects on both CBRS and SBRS. These effects should be kept in mind when nitrates are used to clinically treat chest pain and acute coronary syndromes and used as vasodilators in experimental settings.

An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.