Automatic selection of optimal Savitzky-Golay filter parameters for Coronary Wave Intensity Analysis

Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy

An improved Savitzky-Golay (S-G) filtering algorithm was developed to denoise the absorption spectroscopy of nitrogen oxide (NO₂). A deep learning (DL) network was introduced to the traditional S-G filtering algorithm to adjust the window size and polynomial order in real time. The self-adjusting and follow-up actions of DL network can effectively solve the blindness of selecting the input filter parameters in digital signal processing. The developed adaptive S-G filter algorithm is compared with the multi-signal averaging filtering (MAF) algorithm to demonstrate its performance. The optimized S-G filtering algorithm is used to detect NO₂ in a mid-quantum-cascade-laser (QCL) based gas sensor system. A sensitivity enhancement factor of 5 is obtained, indicating that the newly developed algorithm can generate a high-quality gas absorption spectrum for applications such as atmospheric environmental monitoring and exhaled breath detection.

Yield-Adjusted Operation for Convolution Filter Denoising

A perfect denoising for measurement shall remove noise, while keeping signal truth, so it is a dual-objective optimization of the signal yield and the noise residue. The frequency difference between the noise and signal is the basis of band-limited filter denoising. The root cause for the sharp peak denoise distortion is the insufficient spectrum sampling because of the scattered frequency distribution, which makes it hard to achieve dual-objective optimization. Thus, this article proposes a four-step operation of the signal yield adjustment for beyond the band-limited system. The first step is identifying the signal and noise levels in raw data, then adjusting the sampling density of high-signal level areas and enriching it by linear interpolation, then smoothing the reshaped profile, which is friendly to the filter, and finally, restoring the deformed one to its original form. An executable script function has fully achieved the whole operation. Some actual sharp spectra (Raman, NMR, laser-induced breakdown spectroscopy, and X-ray diffraction) make a comparison between the way with the Savitzky-Golay (SG) method and wavelet (multi-scale) denoising. The results show that all the effects are better than those of the SG filter, all estimations of the yield of signals are more than 99%, and the residue of noise is less than 10%. With multi-scale denoising, this operation is more targeted and gets more rational spectrum profiles─noise reduction without spectrum distortion.

Impact of coronary artery disease on contractile function and ventricular-arterial coupling during exercise: Simultaneous assessment of left-ventricular pressure-volume and coronary pressure and flow during cardiac catheterization

Coronary artery disease (CAD) can adversely affect left ventricular (LV) performance during exercise by impairment of contractile function in the presence of increasing afterload. By performing invasive measures of LV pressure–volume and coronary pressure and flow during exercise, we sought to accurately measure this with comparison to the control group. Sixteen patients, with CCS class >II angina and CAD underwent invasive simultaneous measurement of left ventricular pressure–volume and coronary pressure and flow velocity during cardiac catheterization. Measurements performed at rest were compared with peak exercise using bicycle ergometry. The LV contractile function was measured invasively using the end‐systolic pressure–volume relationship, a load independent marker of contractile function (Ees). Vascular afterload forces were derived from the ratio of LV end‐systolic pressure to stroke volume to generate arterial elastance (Ea). These were combined to assess cardiovascular performance (ventricular‐arterial [VA] coupling ratio [Ea/Ees]). Eleven patients demonstrated flow‐limiting (FL) CAD (hyperemic Pd/Pa <0.80; ST‐segment depression on exercise); five patients without flow‐limiting (NFL) CAD served as the control group. Exercise in the presence of FL CAD was associated impairment of Ees, increased Ea, and deterioration of VA coupling. In the control cohort, exercise was associated with increased Ees and improved VA coupling. The backward compression wave energy directly correlated with the magnitude contraction as measured by dP/dTmax (r = 0.88, p = 0.004). This study demonstrates that in the presence of flow‐limiting CAD, exercise to maximal effort can lead to impairment of LV contractile function and a deterioration in VA coupling compared to a control cohort.

A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.

Accurate and Standardized Coronary Wave Intensity Analysis

OBJECTIVE

Coronary wave intensity analysis (cWIA) has increasingly been applied in the clinical research setting to distinguish between the proximal and distal mechanical influences on coronary blood flow. Recently, a cWIA-derived clinical index demonstrated prognostic value in predicting functional recovery postmyocardial infarction. Nevertheless, the known operator dependence of the cWIA metrics currently hampers its routine application in clinical practice. Specifically, it was recently demonstrated that the cWIA metrics are highly dependent on the chosen Savitzky-Golay filter parameters used to smooth the acquired traces. Therefore, a novel method to make cWIA standardized and automatic was proposed and evaluated in vivo.

METHODS

The novel approach combines an adaptive Savitzky-Golay filter with high-order central finite differencing after ensemble-averaging the acquired waveforms. Its accuracy was assessed using in vivo human data. The proposed approach was then modified to automatically perform beat wise cWIA. Finally, the feasibility (accuracy and robustness) of the method was evaluated.

RESULTS

The automatic cWIA algorithm provided satisfactory accuracy under a wide range of noise scenarios (≤10% and ≤20% error in the estimation of wave areas and peaks, respectively). These results were confirmed when beat-by-beat cWIA was performed.

CONCLUSION

An accurate, standardized, and automated cWIA was developed. Moreover, the feasibility of beat wise cWIA was demonstrated for the first time.

SIGNIFICANCE

The proposed algorithm provides practitioners with a standardized technique that could broaden the application of cWIA in the clinical practice as enabling multicenter trials. Furthermore, the demonstrated potential of beatwise cWIA opens the possibility investigating the coronary physiology in real time.