Literature DB >> 25594987

Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals.

Mikko Paukkunen, Petteri Parkkila, Tero Hurnanen, Mikko Pänkäälä, Tero Koivisto, Tuomo Nieminen, Raimo Kettunen, Raimo Sepponen.   

Abstract

The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart.

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Year:  2015        PMID: 25594987     DOI: 10.1109/JBHI.2015.2391437

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  7 in total

1.  Automatic Identification of Systolic Time Intervals in Seismocardiogram.

Authors:  Ghufran Shafiq; Sivanagaraja Tatinati; Wei Tech Ang; Kalyana C Veluvolu
Journal:  Sci Rep       Date:  2016-11-22       Impact factor: 4.379

2.  SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine.

Authors:  Marco Di Rienzo; Giovannibattista Rizzo; Zeynep Melike Işılay; Prospero Lombardi
Journal:  Sensors (Basel)       Date:  2020-01-26       Impact factor: 3.576

3.  Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients?

Authors:  Zeynep Melike Işilay Zeybek; Vittorio Racca; Antonio Pezzano; Monica Tavanelli; Marco Di Rienzo
Journal:  Front Physiol       Date:  2022-03-18       Impact factor: 4.566

Review 4.  Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions.

Authors:  Jadyn Cook; Muneebah Umar; Fardin Khalili; Amirtahà Taebi
Journal:  Bioengineering (Basel)       Date:  2022-04-01

Review 5.  Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications.

Authors:  Francesca Santucci; Daniela Lo Presti; Carlo Massaroni; Emiliano Schena; Roberto Setola
Journal:  Sensors (Basel)       Date:  2022-08-03       Impact factor: 3.847

6.  An Enhanced Method to Estimate Heart Rate from Seismocardiography via Ensemble Averaging of Body Movements at Six Degrees of Freedom.

Authors:  Hyunwoo Lee; Hana Lee; Mincheol Whang
Journal:  Sensors (Basel)       Date:  2018-01-15       Impact factor: 3.576

7.  Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

Authors:  Hyunwoo Lee; Mincheol Whang
Journal:  Sensors (Basel)       Date:  2018-05-01       Impact factor: 3.576

  7 in total

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