Postural and longitudinal variability in seismocardiographic signals.

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing.Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally.Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5-50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different,Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.

Generation of Pig Airways using Rules Developed from the Measurements of Physical Airways.

BACKGROUND: A method for generating bronchial tree would be helpful when constructing models of the tree for benchtop experiments as well as for numerical modeling of flow or sound propagation in the airways. Early studies documented the geometric details of the human airways that were used to develop methods for generating human airway tree. However, methods for generating animal airway tree are scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. Hence, using algorithms for the human airways may not be accurate in generating models of animal airway geometry. OBJECTIVE: The objective of this study is to develop an algorithm for generating pig airway tree based on the geometric details extracted from the physical measurements. METHODS: In the current study, measured values of branch diameters, lengths and bifurcation angles and rotation of bifurcating planes were used to develop an algorithm that is capable of generating a realistic pig airway tree. RESULTS: The generation relations between parent and daughter branches were found to follow certain trends. The diameters and the length of different branches were dependent on airway generations while the bifurcation angles were primarily dependent on bifurcation plane rotations. These relations were sufficient to develop rules for generating a model of the pig large airways. CONCLUSION: The results suggested that the airway tree generated from the algorithm can provide an approximate geometric model of pig airways for computational and benchtop studies.