| 1. |
- Albano, Amanda, et al.
(författare)
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Echocardio-variability - Low and high frequency beat-to-beat variability in echocardiographic signals
- 2013
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Ingår i: Computing in Cardiology 2013. - 9781479908844 ; , s. 767-770
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Konferensbidrag (refereegranskat)abstract
- Measurement signals originating from the cardiovascular system are known to comprise oscillating components and beat-to-beat variability, e.g., heart-rate variability and blood pressure variability. In clinical echocardiographic procedures, typically only a few cardiac cycles are acquired. This pilot study analyses the beat-to-beat variability of echocardiographic variables (echocardio-variability) in minute long acquisitions.
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| 2. |
- Grönlund, Christer, 1975-, et al.
(författare)
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Significant beat-to-beat variability of E/e’ irrespective of respiration
- 2013
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Ingår i: International cardiovascular forum. - : Barcaray Publishing. - 2409-3424 .- 2410-2636. ; 1:2, s. 88-89
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Tidskriftsartikel (refereegranskat)abstract
- The E/e’ ratio is commonly used in Doppler echocardiographic examinations to estimate the pulmonary capillary wedge pressure. The rationale of using this ratio is to combine left ventricular (LV) filling (E) and relaxation (e’) velocities to indirectly assess left atrial pressure. However, the accuracy of this index has recently been questioned, particularly in patients with controlled heart failure. Likewise, the potential beat-to-beat variability of such measurements remains undetermined. The cardiovascular system is subject to several oscillations with the potential of influencing LV function and its intra-cavitary pressures, hence measurements of its filling and relaxation velocities. The aim of this pilot study was to assess the beat-to-beat variability of the E/e’ ratio in one minute long examination in healthy subjects, and patients with various severity of amyloid heart disease. The results show that despite critical application of the standard echocardiographic recording recommendations, E/e’ beat-to-beat variability was 36 % (22 to 50%) in healthy subjects and 17 % (11-26%) in patients, and where the most severe amyloid heart disease had the least variability. Thus, clinical use of a single or few cardiac beats might not necessarily reflect an accurate ratio between the two velocities, and hence casts doubt over their diagnostic value.
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| 3. |
- Wikström, Conny, et al.
(författare)
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Multivariate process and quality monitoring applied to an electrolysis process. : Part II - Multivariate time-series analysis of lagged latent variables
- 1998
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42:1-2, s. 233-240
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.
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| 4. |
- Wikström, Conny, et al.
(författare)
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Multivariate process and quality monitoring applied to an electrolysis process. : Part I - Process supervision with multivariate control charts
- 1998
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42, s. 221-231
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.
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