Literature DB >> 25194717

Heart monitoring systems--a review.

Puneet Kumar Jain1, Anil Kumar Tiwari2.   

Abstract

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cardiography; Cardiovascular diseases; Electrocardiography; Heart monitoring system; Phonocardiography; Photoplethysmography; Seismocardiography

Mesh:

Year:  2014        PMID: 25194717     DOI: 10.1016/j.compbiomed.2014.08.014

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  7 in total

1.  SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation.

Authors:  Mohammad Tariqul Islam; Sk Tanvir Ahmed; Celia Shahnaz; Shaikh Anowarul Fattah
Journal:  Med Biol Eng Comput       Date:  2018-10-22       Impact factor: 2.602

Review 2.  Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.

Authors:  Brian C S Loh; Patrick H H Then
Journal:  Mhealth       Date:  2017-10-19

3.  Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters.

Authors:  Arash Shokouhmand; Nicole D Aranoff; Elissa Driggin; Philip Green; Negar Tavassolian
Journal:  Sci Rep       Date:  2021-12-10       Impact factor: 4.379

4.  Intelligent Medical Garments with Graphene-Functionalized Smart-Cloth ECG Sensors.

Authors:  Murat Kaya Yapici; Tamador Elboshra Alkhidir
Journal:  Sensors (Basel)       Date:  2017-04-16       Impact factor: 3.576

5.  Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography.

Authors:  Yannick D'Mello; James Skoric; Shicheng Xu; Philip J R Roche; Michel Lortie; Stephane Gagnon; David V Plant
Journal:  Sensors (Basel)       Date:  2019-08-08       Impact factor: 3.576

6.  Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras.

Authors:  Nunzia Molinaro; Emiliano Schena; Sergio Silvestri; Carlo Massaroni
Journal:  Sensors (Basel)       Date:  2022-03-25       Impact factor: 3.576

7.  Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly.

Authors:  Luis J Mena; Vanessa G Félix; Alberto Ochoa; Rodolfo Ostos; Eduardo González; Javier Aspuru; Pablo Velarde; Gladys E Maestre
Journal:  Comput Math Methods Med       Date:  2018-05-29       Impact factor: 2.238

  7 in total

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