Puttarak, Nattakan

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Puttarak, Nattakan
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Puttarak, N.
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nattakan.pu@kmitl.ac.th

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    Non-invasive Optical Blood Glucose Measuring System using Regression Models
    (2023-01-01)
    Limpiti, Tulaya
    ;
    Seemavijai, Mayravee
    ;
    Pongmee, Narutchai
    ;
    Kanayat, Pannita
    ;
    Koseeyaporn, Jeerasuda
    Diabetes is one of the chronic diseases with an increasing number of patients every year. Therefore, blood glucose level (BGL) is an essential health information for diagnosis and management of diabetes. Commonly, the BGL is usually measured invasively by taking blood samples, which can cause pain or discomfort to the patients. Our paper aims to design and develop a low-cost, low-computational non-invasive blood glucose measuring system using optical technique. The proposed system consists of a hardware for measuring photoplethysmogram (PPG). The PPG signal is subsequently analyzed to estimate the corresponding BGL, which is sent wirelessly to be stored in a database and displayed on an Android application. The application recommends the appropriate dosage of insulin injection based on the type of users and their diets. The report of historical blood glucose levels in the database can also be generated for further medical examination.Three PPG features, namely peak-to-peak amplitude, maximum amplitude, and standard deviation of signal amplitude, have the strongest correlations to the BGL. We investigate three regression models for estimating the BGL from these PPG parameters. The robustness of the system is assessed using cross validations. The average root mean squares error (RMSE) for three-fold cross-validations are 17.38, 16.76, and 16.39 mg/dL for simple linear regression, principal component regression, and partial least squares regression, respectively. The RMSEs from leave-one-out cross validation are approximately 12 mg/dL, with partial least squares regression model having the best accuracy. Furthermore, the results from Clarke Error Grid Analysis indicate that the system can be implemented with any of the three models for practical usage.
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    Cardiac Arrhythmia Teletriage using Electrocardiogram
    (2021-01-01)
    Limpiti, Tulaya
    ;
    Chokchaichumnankit, Thunchanok
    ;
    Sanguanchom, Jirayu
    ;
    Soyphan, Natchanon
    ;
    Puttarak, Nattakan
    Telehealth has become a favorable method for receiving medical care during the COVID-19 pandemic. It reduces physical contact and also benefits those who live a distance from hospitals. In this paper we present a cardiac arrhythmia teletriage using electrocardiogram (ECG). The system consists of a diagnostic algorithm for arrhythmias and an Android application. The diagnostic algorithm can detect five types of cardiac problems-arrhythmia, bradycardia, tachycardia, bradyarrhythmia, and tachyarrhythmia. The Android application is the main communication channel between patients and healthcare providers. The user uploads their ECG and receives a preliminary diagnosis of their heart health via the application. The system notifies the user if it detects any abnormalities. The user can then make an appointment online for further examination at the hospital. The capability of the proposed system is evaluated using four databases from PhysioNet - MITBIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and CU Ventricular Tachyarrhythmia Database. It is found that the algorithm is able to detect abnormal ECG signals with an average accuracy of 82.1%.
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    A short-term rain-induced attenuation model for satellite link quality prediction
    (2014-01-01)
    Poolsawut, Jirutchaya
    ;
    Limpiti, Tulaya
    ;
    Puttarak, Nattakan
    This paper proposes a novel but simple satellite link quality prediction based on the relationship between rainfall rate and satellite signal attenuation. After quantifying their relationship statistically, we describe their contemporaneous variations using a linear dynamical system response model. The expectation-maximization (EM) algorithm is used to obtain the maximum likelihood estimates of the model parameters. Subsequently, the communication link quality is predicted by thresholding the estimated short-term signal attenuation level obtained from the Expectation step of the EM algorithm. The prediction performance of the proposed method is compared against the measured beacon signals from Thaicom-4 (IPSTAR) satellite during rainfalls. Using solely the observed rainfall rates from a tipping bucket rain gauge, the algorithm is able to decently predict the satellite link quality. We believe the proposed model can be modified as a predictive tool for real-time link unavailability monitoring of satellite communication system. © 2014 IEEE.
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    A study on Ka-band site switching using advanced prediction for rain-induced attenuation
    (2015-01-28)
    Poolsawut, Jirutchaya
    ;
    Limpiti, Tulaya
    ;
    Puttarak, Nattakan
    High frequency satellite communications suffers from rain-induced attenuations, which may cause service discontinuity during high-intensity rain events. To combat this, we propose a novel site switching technique that anticipates signal attenuations using a linear dynamical system response model and performs site switching in advance. Using only the rainfall data measured at a ground station for attenuation prediction, we demonstrate that our method is able to lessen signal attenuation while maintaining link availability.
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    A smart dialogue box for the hearing impaired
    (2019-07-01)
    Limpiti, Tulaya
    ;
    Khamyan, Sudarat
    ;
    Chumsaeng, Setthawut
    ;
    Puttarak, Nattakan
    Conversations between a person with severe hearing impairment and a hearing person is difficult because people who know sign language are rare. Therefore, to facilitate the conversation, we design and develop a portable dialogue box that can be used without an interpreter. The system detects sign language of a deaf person by capturing hand images with a camera. The images are pre-processed and the histograms of oriented gradients (HOG) are extracted as image features. A multi-class support vector machine uses the HOGs as inputs to classify the alphabets. The vocal reply from a hearing person is received through a microphone and interpreted via Google cloud speech API. Both parts are processed by a microcomputer to produce messages of the conversation. The dialogue is displayed on the graphical user interface. The system can also record the conversation logs into the database.
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    Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study
    (2021-01-01)
    Limpiti, Tulaya
    ;
    Seetanathum, Kornthum
    ;
    Sricom, Natchaya
    ;
    Puttarak, Nattakan
    Motor imagery (MI) is the cognitive process when a person imagines performing a specific movement of their body. The corresponding electroencephalographic (EEG) signals can be measured externally to the head using scalp electrodes. Such signals have been applied in healthcare brain-computer-interface (BCI) systems, for example, motor rehabilitation and prosthetics control. These systems convert different MI EEG input signals to directives, so their performances depend on the efficiency of the embedded signal classification algorithm. In this paper we investigate the effectiveness of transfer learning in classifying the MI EEG data. The Continuous Wavelet Transform (CWT) is used to construct the scalograms, which serve as the inputs to the deep learning structure. The efficacies of five pre-trained networksAlexNet, ResNet18, ResNet50, InceptionV3 and ShuffleNet-are evaluated on the BCI competition IV data set 2a. Binary (left hand vs. right hand) and four-class (left hand, right hand, both feet, and tongue) classifications are trained and tested using fivefold cross validation. The result indicates that using the CWT with transfer learning models provides very high classification accuracies. The ResNet18 network achieves the best accuracies in both cases at 95.03±2.95% and 91.86±2.90%, respectively. In addition, we examine the effect of different time-frequency features on the classification performance by comparing the scalogram of the CWT and the spectogram of the Short-Time Fourier Transform (STFT) as the inputs. It is found that the CWT is the preferred choice as it is superior to the STFT.