Heart energy signature spectrogram for cardiovascular diagnosis

The steadily growing elderly population calls for efficient, reliable and preferably ambulant health supervision. Since cardiovascular risk factors interact with psychosocial strain (e.g., depression), we investigated the potential contribution of psychosocial factors in discriminating generally healthy elderly from those with a cardiovascular condition, on and above routinely applied physiological assessments. Fifteen elderly (aged 60 to 88) with a cardiovascular diagnosis were compared to fifteen age and gender matched healthy peers. Six sequential standardized lab assessments were conducted (one every two weeks), including an autonomic test battery, a 6-min step test and questionnaires covering perceived psychological state and experiences over the previous two weeks. Specific combinations of physiological and psychological factors (most prominently symptoms of depression) effectively predicted (clinical) cardiovascular markers. Additionally, a highly significant prognostic model was found, including depressive symptoms, recently experienced negative events and social isolation. It appeared slightly superior in identifying elderly with or without a cardiovascular condition compared to a model that only included physiological parameters. Adding psychosocial parameters to cardiovascular assessments in elderly may consequently provide protocols that are significantly more efficient, relatively comfortable and technologically feasible in ambulant settings, without necessarily compromising prognostic accuracy.

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Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽

10.3390/en13051158 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1158

Author(s):

Behrad Bezyan ◽

Radu Zmeureanu

Keyword(s):

Machine Learning ◽

Energy Demand ◽

Energy Use ◽

Machine Learning Techniques ◽

Northern Canada ◽

Automation And Control ◽

And Performance ◽

Energy Signature ◽

Heating Energy Demand ◽

And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

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Cardiovascular Diagnosis and Therapy is indexed in SCIE: the new beginning

Cardiovascular Diagnosis and Therapy ◽

10.21037/cdt.2019.01.03 ◽

2019 ◽

Vol 9 (1) ◽

pp. 115-119

Author(s):

Keyword(s):

Diagnosis And Therapy ◽

New Beginning ◽

Cardiovascular Diagnosis

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PCG Spectral Pattern Classification: Approach to Cardiac Energy Signature Identification

IFMBE Proceedings - 13th International Conference on Biomedical Engineering ◽

10.1007/978-3-540-92841-6_148 ◽

2009 ◽

pp. 602-605

Author(s):

Abbas K. Abbas ◽

Rasha Bassam

Keyword(s):

Pattern Classification ◽

Classification Approach ◽

Spectral Pattern ◽

Cardiac Energy ◽

Energy Signature

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Developing the New Proposal of Intelligence System in Heart and Cardiovascular Diagnosis

2019 6th NAFOSTED Conference on Information and Computer Science (NICS) ◽

10.1109/nics48868.2019.9023903 ◽

2019 ◽

Author(s):

Hieu Le Ngoc ◽

Hung Tran Cong ◽

Sang Do Phuoc ◽

Khue Bui Dan

Keyword(s):

Intelligence System ◽

Cardiovascular Diagnosis

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Analysis of energy signatures and planning of heating and domestic hot water energy use in buildings in Norway

E3S Web of Conferences ◽

10.1051/e3sconf/201911106009 ◽

2019 ◽

Vol 111 ◽

pp. 06009

Author(s):

Tymofii Tereshchenko, ◽

Dmytro Ivanko ◽

Natasa Nord ◽

Igor Sartori

Keyword(s):

Energy Use ◽

District Heating ◽

Energy System ◽

Energy Performance ◽

Multiple Regression Model ◽

Hot Water ◽

Temperature Dependent ◽

Domestic Hot Water ◽

Dependent Part ◽

Energy Signature

Widespread introduction of low energy buildings (LEBs), passive houses, and zero emission buildings (ZEBs) are national target in Norway. In order to achieve better energy performance in these types of buildings and successfully integrate them in energy system, reliable planning and prediction techniques for heat energy use are required. However, the issue of energy planning in LEBs currently remains challenging for district heating companies. This article proposed an improved methodology for planning and analysis of domestic hot water and heating energy use in LEBs based on energy signature method. The methodology was tested on a passive school in Oslo, Norway. In order to divide energy signature curve on temperature dependent and independent parts, it was proposed to use piecewise regression. Each of these parts were analyzed separately. The problem of dealing with outliers and selection of the factors that had impact of energy was considered. For temperature dependent part, the different methods of modelling were compared by statistical criteria. The investigation showed that linear multiple regression model resulted in better accuracy in the prediction than SVM, PLS, and LASSO models. In order to explain temperature independent part of energy signature the hourly profiles of energy use were developed.

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