Music-based Graph Convolution Neural Network with ECG, Respiration, Pulse Signal as a Diagnostic Tool for Hypertension

Poulomi Pal, Natalia Cotic, Mateusz Solinski, Vanessa Pope, Pier Lambiase, Elaine Chew*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Hypertension is one of the prime risk factors of cardiovascular disease. Music has been shown to be beneficial for lowering blood pressure. Here, we investigate if music can help in identifying hypertensive individuals. We acquire simultaneously electrocardiography (ECG), respiration, and pulse signals from 70 participants whilst they listen to music that has been altered digitally to have the same notes but differing tempi and loudness. Blood pressure (BP) values obtained during the preceding silent period was taken as ground truth. After pre-processing, we obtain feature indices E,R,P from the ECG, respiration and pulse signals, respectively. The indices are fused to derive the compound indices ERP, EP, RP, and ER. Classification was performed using GCNN (Graph Convolution Neural Network) to segregate hypertensives from normal individuals. The index values formed the nodes and the music attributes average tempo and loudness were used to establish the edge connectivity for node based classification. Binary classification was carried out with 0.85 accuracy, 0.87 recall, 0.84 specificity, and 0.86 F1-score. We demonstrate for the first time the potential of music hypertension diagnosis using listeners’ ECG, respiration, and pulse signal.
Original languageEnglish
Title of host publicationMusic-based Graph Convolution Neural Network with ECG, Respiration, Pulse Signal as a Diagnostic Tool for Hypertension
Publication statusAccepted/In press - 23 Oct 2024

Keywords

  • deep learning
  • music physiology
  • music theranostics
  • music-based diagnostics
  • psychophysiology
  • music perception
  • cardiovascular science
  • hypertension

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