Extraction of periodic multivariate signals: mapping of voltage-dependent dye fluorescence in the mouse heart

• Published in: IEEE transactions on medical imaging Vol. 22; no. 12; pp. 1537 - 1549
• Main Authors: Sornborger, A., Sirovich, L., Morley, G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2003; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action Potentials; Algorithms; Animals; Biological and medical sciences; Biological Clocks; Cardiac arrhythmia; Data mining; Data visualization; Diagnosis, Computer-Assisted - methods; Electrocardiography - methods; Fluorescence; Fluorescent Dyes; Harmonic analysis; Heart; Heart Conduction System - pathology; Heart Conduction System - physiopathology; Heart Rate; High-resolution imaging; In Vitro Techniques; Medical sciences; Mice; Microscopy, Fluorescence - methods; Periodicity; Pyridinium Compounds; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Reproducibility of Results; Rhythm; Sensitivity and Specificity; Signal analysis; Signal mapping; Signal Processing, Computer-Assisted; Tachycardia - diagnosis; Tachycardia - physiopathology; Technology. Biomaterials. Equipments. Material. Instrumentation; Voltage; Biomedical engineering
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.818163

In many experimental circumstances, heart dynamics are, to a good approximation, periodic. For this reason, it makes sense to use high-resolution methods in the frequency domain to visualize the spectrum of imaging data of the heart and to estimate the deterministic signal content and extract the periodic signal from background noise in experimental data. In this paper, we describe the first application of a new method that we call cardiac rhythm analysis which uses a combination of principal component analysis and multitaper harmonic analysis to extract periodic, deterministic signals from high-resolution imaging data of cardiac electrical activity. We show that this method significantly increases the signal-to-noise ratio of our recordings, allowing for better visualization of signal dynamics and more accurate quantification of the properties of electrical conduction. We visualize the spectra of three cardiac data sets of mouse hearts exhibiting sinus rhythm, paced rhythm and monomorphic tachycardia. Then, for pedagogical purposes, we investigate the tachycardia more closely, demonstrating the presence of two distinct periodicities in the re-entrant tachycardia. Analysis of the tachycardia shows that cardiac rhythm analysis not only allows for better visualization of electrical activity, but also provides new opportunities to study multiple periodicities in signal dynamics.