Scalability of cardiovascular intrinsic frequencies: Validations in preclinical models and non-invasive clinical studies

Alavi, Rashid; Dai, Wangde; Amlani, Faisal; Rinderknecht, Derek; Kloner, Robert A.; Pahlevan, Niema M.

doi:10.1016/j.lfs.2021.119880

Cited by 9 publications

(15 citation statements)

References 33 publications

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“…The non-dimensional ratio R s / R d of the two envelope constants is known as the envelope ratio ( ER ) [21, 33]. The systolic IF parameters (ω 1 , φ 1 , R s ) are dominated by the dynamics of the coupled LV-arterial system, whereas the diastolic IF parameters (ω 2 , φ 2 , R d ) are dominated by the dynamics of just the arterial network [23, 24, 26-28].…”

Section: Methodsmentioning

confidence: 99%

“…Such a normalization is important to ensure that the developed model is sensor (or device) agnostic, especially since the heterogeneous clinical databases contain measurements taken by different apparatus (whose raw waveforms can be of arbitrary units). As such, a new standard coordinate system has been proposed [33] for arterial waveforms through which measurements of different devices (and even different species [33]) fall within the same range of signals and IF parameters, enabling the inclusion of any arbitrary raw arterial signal. In this spirit, a further normalization in time enables the mapping of all arterial waveforms onto the same length of the cardiac cycle (T′=1).…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1A illustrates how the IF-reconstructed waveform represents the original pressure waveform. Further details regarding the mathematical formulation, computational procedure, scalability, convergence/accuracy, and applicability on non-invasive measurements of the IF method have been provided in previous studies [20, 27, 30, 32, 33].…”

Section: Methodsmentioning

confidence: 99%

“…For an arterial waveform 𝑝(𝑡) (e.g., of the aortic, carotid, or femoral), the IF method solves [20,30] a nonlinear optimization problem (L2-minimization) for a cardiac cycle of length 𝑇 through the objective function that is given by: Further details regarding the mathematical formulation, computational procedure, scalability, convergence/accuracy, and applicability on non-invasive measurements of the IF method have been provided in previous studies [20,27,30,32,33].…”

Section: Calculation Of Intrinsic Frequencies)mentioning

confidence: 99%

Section: Waveform Normalization Proceduresmentioning

confidence: 99%

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Sequentially-reduced representation of artificial neural network to determine cardiovascular intrinsic frequencies

Alavi

Wang

Gorji

et al. 2022

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Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L 2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L 2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L 2 optimization-based method (i.e., R [[EQUATION]] 0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.

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