UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Cardiac Response to Live Music Performance: Computing Techniques for Feature Extraction and Analysis

Chew, E; Taggart, P; Lambiase, P; (2020) Cardiac Response to Live Music Performance: Computing Techniques for Feature Extraction and Analysis. In: Proceedings of the 2019 Computing in Cardiology (CinC). (pp. pp. 1-4). IEEE Green open access

[img]
Preview
Text
CinC2019-445.pdf - Published version

Download (614kB) | Preview

Abstract

Strong emotions and mental stress have been linked to potentially deadly arrhythmias. Music evokes strong emotion through the regulation of tension and release and the modulation of changes and transitions. We exploit this in a novel study involving patients with implanted cardiac defibrillators to study the impact of live music performance on cardiac electrophysiology. The patients’ heart rates are artificially fixed with pacing at the higher of 80 beats per minute or 10 above the heart’s intrinsic rate. We make continuous recordings directly from the heart muscle whilst the patients are listening to a short classical music concert, which is concurrently recorded in a separate stream. The participants provide annotations of perceived boundaries/transitions and felt tension. The recorded cardiac and music information is further processed to extract relevant features. Here, we describe the experiment design, and the mathematical and computing techniques used to represent and abstract the features from the recorded data. Cardiac reaction is measured by the action potential duration (APD), approximated using the action recovery interval (ARI). The expressive parameters extracted from the music include the time varying loudness, tempo, and harmonic tension. The synchronized information layers allow for detailed analysis of immediate cardiac response to dynamically varying expressive nuances in performed music.

Type: Proceedings paper
Title: Cardiac Response to Live Music Performance: Computing Techniques for Feature Extraction and Analysis
Event: 2019 Computing in Cardiology (CinC)
Location: Singapore
Dates: 8th-11th September 2019
ISBN-13: 978-1-7281-6936-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/CinC49843.2019.9005842
Publisher version: http://www.cinc.org/archives/2019/pdf/CinC2019-445...
Language: English
Additional information: © 2019 Computing in Cardiology. CinC has been an open-access publication, in which copyright in each article is held by its authors, who grant permission to copy and redistribute their work with attribution, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Music, Stress, Heart beat, Avalanche photodiodes, Multiple signal classification, Harmonic analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Clinical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10094262
Downloads since deposit
26Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item