Effect of Hemodialysis on Left Ventricular Performance

Geleris, Paraschos; Raidis, C.; Papadimitriou, Maria; Bougoulas, H.; Metaxas, P

doi:10.1016/s0022-5347(17)50794-6

Journal of Urology

1984

DOI: 10.1016/s0022-5347(17)50794-6

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Effect of Hemodialysis on Left Ventricular Performance

Paraschos Geleris

C. Raidis

Maria Papadimitriou

et al.

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2016

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“…For instance, increased PEP and decreased LVET are shown in heart failure patients as compared to the normal subjects29. STI has been useful in numerous applications such as cardiac computed tomographic gating30, identification of exercise capacity31, optimization of cardiac resynchronization therapy in heart failure patients24, identifying extent of left ventricular (LV) muscle dysfunction24, observing changes in LV performance during haemodialysis32, mitral valve stenosis33, atrial fibrillation34, coronary artery disease35 and detection of ischemia1736 etc. Further, in the context of personal health monitoring from wearable sensors, non-invasive and unobtrusive measurement of STI can provide a quantified measure of cardiac health2428.…”

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confidence: 99%

Automatic Identification of Systolic Time Intervals in Seismocardiogram

Shafiq

Tatinati

Ang

et al. 2016

Sci Rep

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Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.

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confidence: 99%

Automatic Identification of Systolic Time Intervals in Seismocardiogram

Shafiq

Tatinati

Ang

et al. 2016

Sci Rep

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Effect of Hemodialysis on Left Ventricular Performance

Cited by 1 publication

References 0 publications

Automatic Identification of Systolic Time Intervals in Seismocardiogram

Automatic Identification of Systolic Time Intervals in Seismocardiogram

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