Projects per year
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 language | English |
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Title of host publication | Music-based Graph Convolution Neural Network with ECG, Respiration, Pulse Signal as a Diagnostic Tool for Hypertension |
Publication status | Accepted/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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COSMOS: COSMOS: Computational Shaping and Modeling of Musical Structures
Chew, E. (Primary Investigator)
1/07/2022 → 30/11/2025
Project: Research
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HeartFM: Maximizing the Therapeutic Potential of Music through Tailored Therapy with Physiological Feedback in Cardiovascular Disease
Chew, E. (Primary Investigator)
1/07/2022 → 31/05/2023
Project: Research
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A Computational Method for Empirically Validating Synchronisation Between Musical Phrase Arcs and Autonomic Variables
Cotic, N., Solinski, M., Pope, V., Lambiase, P. & Chew, E., 8 Sept 2024, (Accepted/In press) A Computational Method for Empirically Validating Synchronisation Between Musical Phrase Arcs and Autonomic Variables. IEEE XploreResearch output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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A framework for modeling performers' beat-to-beat heart intervals using music features and Interpretation Maps
Soliński, M., Reed, C. N. & Chew, E., 4 Sept 2024, In: Frontiers in Psychology. 15, p. 1403599 10 p., 1403599.Research output: Contribution to journal › Article › peer-review
Open Access -
Raised Blood Pressure Alters Reactivity to Musical Features
Pope, V., Solinski, M., Lambiase, P. D. & Chew, E., 31 Aug 2024, Raised Blood Pressure Alters Reactivity to Musical Features.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open AccessFile