当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

Abstract：

:The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive and non-invasive hemodynamic monitoring techniques. These methods require expert supervision and costly clinical set-ups, and cannot be employed by a common individual to perform a self diagnosis of one's cardiac health, unassisted. In this work, the authors propose a novel methodology using impedance cardiography (ICG), for the determination of a person's cardio-vascular health. The recorded ICG signal helps in extraction of features which are used for estimating parameters for cardiac health monitoring. The proposed methodology with the aid of artificial neural network is able to determine Stroke Volume (SV), Left Ventricular End Systolic Volume (LVESV), Left Ventricular End Diastolic Volume (LVEDV), Left Ventricular Ejection Fraction (LVEF), Iso Volumetric Contraction Time (IVCT), Iso Volumetric Relaxation Time (IVRT), Left Ventricular Ejection Time (LVET), Total Systolic Time (TST), Total Diastolic Time (TDT), and Myocardial Performance Index (MPI), with error margins of ±8.9%, ±3.8%, ±1.4%, ±7.8%, ±16.0%, ±9.0%, ±9.7%, ±6.9%, ±6.2%, and ±0.9%, respectively. The proposed methodology could be used in screening of precursors to cardiac ailments, and to keep a check on the cardio-vascular health.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Ghosh S,Chattopadhyay BP,Roy RM,Mukherjee J,Mahadevappa M

doi

10.1016/j.artmed.2019.02.002

subject

Has Abstract

pub_date

2019-05-01 00:00:00

pages

45-58

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(18)30465-2

journal_volume

96

pub_type

杂志文章

相关文献

ARTIFICIAL INTELLIGENCE IN MEDICINE文献大全
  • Constructing explanatory process models from biological data and knowledge.

    abstract：OBJECTIVE:We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS:We cast both models and background knowledge in terms of processes that interact to account for behavior. We a...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2006.04.003

    authors： Langley P,Shiran O,Shrager J,Todorovski L,Pohorille A

    更新日期：2006-07-01 00:00:00

  • Case-based distance measurements for the selection of controls in case-matched studies: application in coronary interventions.

    abstract：:In case-based studies, controls are retrospectively assigned to patients in order to permit a statistical evaluation of the study results through a comparison of the main outcome measures for the patient and retrieved control groups. Inappropriate selection of the controls by using false retrieval parameters or a fals...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(02)00084-2

    authors： Gyöngyösi M,Ploner M,Porenta G,Sperker W,Wexberg P,Strehblow C,Glogar D

    更新日期：2002-11-01 00:00:00

  • Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

    abstract：:Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc.) or the acquisition technology, which mi...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101804

    authors： Ferrari E,Retico A,Bacciu D

    更新日期：2020-03-01 00:00:00

  • Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs.

    abstract：OBJECTIVE:We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS:Both time and f...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2016.01.005

    authors： Yin XX,Hadjiloucas S,Zhang Y,Su MY,Miao Y,Abbott D

    更新日期：2016-02-01 00:00:00

  • A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.

    abstract：OBJECTIVE:This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multi...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.02.002

    authors： Schipper JD,Dankel DD 2nd,Arroyo AA,Schauben JL

    更新日期：2013-05-01 00:00:00

  • Finding temporal patterns--a set-based approach.

    abstract：:We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(94)90066-3

    authors： Wade TD,Byrns PJ,Steiner JF,Bondy J

    更新日期：1994-06-01 00:00:00

  • Modern parameterization and explanation techniques in diagnostic decision support system: a case study in diagnostics of coronary artery disease.

    abstract：OBJECTIVE:Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary arter...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.04.009

    authors： Kukar M,Kononenko I,Grošelj C

    更新日期：2011-06-01 00:00:00

  • A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images.

    abstract：:According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical c...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101980

    authors： Bennai MT,Guessoum Z,Mazouzi S,Cormier S,Mezghiche M

    更新日期：2020-11-01 00:00:00

  • Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model.

    abstract：:The process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(97)00020-1

    authors： Becker K,Thull B,Käsmacher-Leidinger H,Stemmer J,Rau G,Kalff G,Zimmermann HJ

    更新日期：1997-09-01 00:00:00

  • Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

    abstract：BACKGROUND:Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101997

    authors： Esmaili N,Buchlak QD,Piccardi M,Kruger B,Girosi F

    更新日期：2021-01-01 00:00:00

  • Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

    abstract：OBJECTIVE:This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation i...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.12.006

    authors： Jiménez F,Sánchez G,Juárez JM

    更新日期：2014-03-01 00:00:00

  • Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

    abstract：:Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progr...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.101742

    authors： Zahia S,Garcia Zapirain MB,Sevillano X,González A,Kim PJ,Elmaghraby A

    更新日期：2020-01-01 00:00:00

  • Relationship between preparation of cells for therapy and cell quality using artificial neural network analysis.

    abstract：OBJECTIVE:The successful preparation of cells for therapy depends on the characterization of causal factors affecting cell quality. Ultra scale-down methods are used to characterize cells in terms of their response to process engineering causal factors of hydrodynamic shear stress and time. This response is in turn cha...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2014.07.003

    authors： Dhondalay GK,Lawrence K,Ward S,Ball G,Hoare M

    更新日期：2014-10-01 00:00:00

  • Brain-computer interface controlled gaming: evaluation of usability by severely motor restricted end-users.

    abstract：OBJECTIVE:Connect-Four, a new sensorimotor rhythm (SMR) based brain-computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement. METHODS:Following the user-centred approach, usability of the BCI prototype was ev...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2013.08.001

    authors： Holz EM,Höhne J,Staiger-Sälzer P,Tangermann M,Kübler A

    更新日期：2013-10-01 00:00:00

  • Identification of the optic nerve head with genetic algorithms.

    abstract：OBJECTIVE:This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. METHODS AND MATERIAL:Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an ey...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.04.005

    authors： Carmona EJ,Rincón M,García-Feijoó J,Martínez-de-la-Casa JM

    更新日期：2008-07-01 00:00:00

  • Selection of patients for clinical trials: an interactive web-based system.

    abstract：:The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual pro...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.017

    authors： Fink E,Kokku PK,Nikiforou S,Hall LO,Goldgof DB,Krischer JP

    更新日期：2004-07-01 00:00:00

  • Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval.

    abstract：OBJECTIVES:We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS:We propose firstly a method for enriching mu...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.07.015

    authors： Déjean H,Gaussier E,Renders JM,Sadat F

    更新日期：2005-02-01 00:00:00

  • Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

    abstract：:Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101911

    authors： Lee WK,Wu CC,Lee CC,Lu CF,Yang HC,Huang TH,Lin CY,Chung WY,Wang PS,Wu HM,Guo WY,Wu YT

    更新日期：2020-07-01 00:00:00

  • Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions.

    abstract：:In this contribution we present an application of a knowledge-based neural network technique in the domain of medical research. We consider the crucial problem of intensive care patients developing a septic shock during their stay at the intensive care unit. Septic shock is of prime importance in intensive care medici...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(03)00057-5

    authors： Paetz J

    更新日期：2003-06-01 00:00:00

  • The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.

    abstract：:The optical device LIPOMETER allows for non-invasive, quick, precise and safe determination of subcutaneous fat distribution, so-called subcutaneous adipose tissue topography (SAT-Top). In this paper, we show how the high-dimensional SAT-Top information of women with type-2 diabetes mellitus (non-insulin-dependent dia...

    journal_title：Artificial intelligence in medicine

    pub_type： 临床试验,杂志文章

    doi：10.1016/s0933-3657(99)00017-2

    authors： Tafeit E,Möller R,Sudi K,Reibnegger G

    更新日期：1999-10-01 00:00:00

  • Information extraction from multi-institutional radiology reports.

    abstract：OBJECTIVES:The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinic...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章,多中心研究

    doi：10.1016/j.artmed.2015.09.007

    authors： Hassanpour S,Langlotz CP

    更新日期：2016-01-01 00:00:00

  • Development of systems for support of collaboration in health care: the design arenas.

    abstract：:To explore the design of computer-supported collaborative work in health care, a case study is described addressing the social contexts and conditions influencing the development process. The data set covers 13 consecutive meetings held in a systems design group over a 2-year period, in total approximately 24 h of vid...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(97)00046-8

    authors： Timpka T,Sjöberg C

    更新日期：1998-02-01 00:00:00

  • Inconsistency as a diagnostic tool in a society of intelligent agents.

    abstract：OBJECTIVE:To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS:We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent ag...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2012.04.005

    authors： McShane M,Beale S,Nirenburg S,Jarrell B,Fantry G

    更新日期：2012-07-01 00:00:00

  • Retinal image assessment using bi-level adaptive morphological component analysis.

    abstract：:The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and trea...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.07.010

    authors： Javidi M,Harati A,Pourreza H

    更新日期：2019-08-01 00:00:00

  • Granular support vector machines with association rules mining for protein homology prediction.

    abstract：OBJECTIVE:Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an ess...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2005.02.003

    authors： Tang Y,Jin B,Zhang YQ

    更新日期：2005-09-01 00:00:00

  • Anatomical sketch understanding: recognizing explicit and implicit structure.

    abstract：OBJECTIVE:Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applicat...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2006.07.010

    authors： Haddawy P,Dailey MN,Kaewruen P,Sarakhette N,Hai le H

    更新日期：2007-02-01 00:00:00

  • ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

    abstract：:Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method fo...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101856

    authors： Zhang J,Liu A,Gao M,Chen X,Zhang X,Chen X

    更新日期：2020-06-01 00:00:00

  • Specification of models in large expert systems based on causal probabilistic networks.

    abstract：:Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between node...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(93)90029-3

    authors： Olesen KG,Andreassen S

    更新日期：1993-06-01 00:00:00

  • Bounded-depth threshold circuits for computer-assisted CT image classification.

    abstract：:We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(01)00101-4

    authors： Albrecht A,Hein E,Steinhöfel K,Taupitz M,Wong CK

    更新日期：2002-02-01 00:00:00

  • Logarithmic simulated annealing for X-ray diagnosis.

    abstract：:We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(00)00112-3

    authors： Albrecht A,Steinhöfel K,Taupitz M,Wong CK

    更新日期：2001-06-01 00:00:00