Beat-to-Beat Systolic Time-Interval Measurement from Heart Sounds and ECG



Systolic time intervals are highly correlated to fundamental cardiac functions. Several studies have shown that these measurements have significant diagnostic and prognostic value in heart failure condition and are adequate for long-term patient follow-up and disease management. In this paper, we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic heart valve. These moments are crucial to define the main systolic timings of the heart cycle, i.e., PEP and LVET. We introduce an algorithm for automatic extraction of PEP and LVET using HS and ECG. PEP is estimated with a Bayesian approach using to the signal’s instantaneous amplitude and patient-specific time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, since the aortic valve closure corresponds to the start of the S2 heart sound component, we base LVET estimation on the detection of the S2 onset. A comparative assessment of the main systolic time intervals is performed using synchronous signal acquisitions of the current gold standard in cardiac time intervals measurement, i.e., echocardiography, and heart sound. The algorithms were evaluated on a healthy population, as well as on a group of subjects with different cardiovascular diseases (CVD). In the healthy group, from a set of 942 heartbeats, the proposed algorithm achieved 7.66 ± 5.92 msec absolute PEP estimation error. For LVET, the absolute estimation error was 11.39 ± 8.98 msec. For the CVD population, 404 beats were used, leading to 11.86 ± 8.30 msec and 17.51 ± 17.21 msec absolute PEP and LVET errors, respectively. The results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.




medical informatics

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Physiological Measurement , Vol. 33, pp. 177-194, IOP Science, January 2012

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Year 2013 : 2 citations

 1. Su, Ho-Ming, et al. (2013). "A Comparison between Brachial and Echocardiographic Systolic Time Intervals." PloS one 8.2: e55840.

 Luprano, J., et al. "HeartCycle: Advanced sensors for telehealth applications." Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013.