@inbook{9f16a82278fc41109b765d1c8b0339f4,
title = "On Making Music with Heartbeats",
abstract = "Representation and analysis of musical structures in heart signals can benefit understanding of cardiac electrophysiology aberrations such as arrhythmias, which can in turn aid in the diagnosis and treatment of cardiac arrhythmias. The typical time-frequency analysis of electrocardiographic recordings of cardiac arrhythmias yield descriptive statistics that provide useful features for classification, but fail to capture the actual rhythms of the physiological phenomena. Here, I propose to use music notation to represent beat-to-beat and morphological feature-to-feature durations of abnormal cardiac rhythms, using articulation markings when emphasis is warranted. The rhythms and articulations captured in these representations may provide cues to differentiate between individual experiences of cardiac arrhythmia, with potential impact on personalising diagnostics and treatment decisions. Music generation is presented as an application of these rhythm transcriptions. The physiological origins ensure that the music based on heart rhythms, even abnormal ones, sound natural. The two-part music creation process draws inspiration from music collage practices, and comprises of a retrieval component followed by transformation processes, which can be applied at the melody or block levels, and complex combinations thereof. The music thus created can not only be used to identify distinct heart signatures and what they mean for different cardiac conditions, they can also provide a visceral record of the experience of an arrhythmic episode. The pounding and fluttering of arrhythmia can often be physically uncomfortable. The music created from arrhythmic traces is not easy listening; it is often provocative, but potentially instructive.",
keywords = "cardiac arrhythmias, music transcription, music representation, electrocardiogram, algorithmic music, artificial intelligence, diagnostic tools",
author = "Elaine Chew",
year = "2021",
month = jul,
day = "3",
language = "English",
isbn = "978-3-030-72115-2",
pages = "237",
booktitle = "Handbook of Artificial Intelligence for Music",
publisher = "Springer, Cham",
edition = "1",
}