Treebupachatsakul, Treesukon

Preferred name

Alternative Name

Treebupachatsakul, T.

Main Affiliation

School of Engineering

treesukon.tr@kmitl.ac.th

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Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training
(2022-03-01)
Treebupachatsakul, Treesukon
;
Boosamalee, Apivitch
;
Shinnakerdchoke, Siratchakrit
;
Pechprasarn, Suejit
;
Thongpance, Nuntachai
This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.
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Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance
(2022-01-01)
Shinnakerdchoke, Siratchakrit
;
Thadson, Kitsada
;
Pechprasarn, Suejit
;
Treebupachatsakul, Treesukon
Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of 3.83 \times 10^{\mathbf{-4}} RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model's accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.
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Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection
(2023-04-01)
Treebupachatsakul, Treesukon
;
Shinnakerdchoke, Siratchakrit
;
Pechprasarn, Suejit
Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.
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Analysis of effects of surface roughness on sensing performance of surface plasmon resonance detection for refractive index sensing application
(2021-09-01)
Treebupachatsakul, Treesukon
;
Shinnakerdchoke, Siratchakrit
;
Pechprasarn, Suejit
This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root‐mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure‐of-merit equation considering optical intensity contrast and signal‐to‐noise ratio. The proposed figure-of‐merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement.

Treebupachatsakul, Treesukon

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