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Wavelet transform analysis reveals differences between patients with impaired left ventricular systolic function and healthy individuals

Gruszecki, M.; Kaufmann, D.; Swiatczak, M.; Mlodzinski, K.; Neary, J. P.; Singh, J.; Ruminski, J.; Danilowicz-Szymanowicz, L.

2023-09-26 cardiovascular medicine
10.1101/2023.09.25.23296125 medRxiv
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BackgroundDespite continuous progress in medical treatment, heart failure (HF) is the leading cause of hospitalizations with a high all-cause mortality in patients. Patients with a left ventricular ejection fraction (LVEF) below 50% are characterized by the highest risk of cardiovascular complications. The objective of this study was to examine how LVEF below 50% and aging impact cardiovascular physiology. MethodsSixteen males with physician diagnosed coronary artery disease and LVEF = 42 {+/-} 6% (age 62 {+/-} 6 years, BMI 29.1 {+/-} 3.8kg/m2) and 10 healthy controls (9 male and 1 female, age 28.5 {+/-} 9.1 years, BMI = 24.1 {+/-} 1.2kg/m2) were recruited in our study. Finger photoplethysmography for blood pressure (BP) and electrocardiogram (ECG) were recorded while participants rested in a supine position. Wavelet transformations were used to analyze the amplitudes, phase coherence and phase difference of BP and ECG. The frequency intervals were separated as follows: I (0.6-2Hz), II (0.145- 0.6Hz), III (0.052-0.145Hz), and IV (0.021-0.052Hz). ResultsHF patients showed a decrease (p<0.05) in BP wavelet amplitude intervals III and IV in comparison to controls, and interval I for ECG. A decrease in phase coherence (p<0.01) at interval I is also found in HF patients compared to controls. ConclusionsA decrease in smooth muscle cell activity and smooth muscle autonomic innervation (intervals III and IV) contributions to BP, along with a decrease in cardiac activity as shown by the wavelet amplitude in ECG, suggests altered BP and ECG function in aging HF patients. Furthermore, a decrease in the cardiac interval represents an impairment in the BP and ECG relationship in HF patients. The wavelet transform has the potential to expand our understanding of LVEF and improve diagnostic procedures and patient prognosis.

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