Tharoeun Thap

Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions

We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes.

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV1/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV1/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV1/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone.

Smartphone-Based Cardiac Rehabilitation Program: Feasibility Study

We introduce a cardiac rehabilitation program (CRP) that utilizes only a smartphone, with no external devices. As an efficient guide for cardiac rehabilitation exercise, we developed an application to automatically indicate the exercise intensity by comparing the estimated heart rate (HR) with the target heart rate zone (THZ). The HR is estimated using video images of a fingertip taken by the smartphone’s built-in camera. The introduced CRP app includes pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up. In the exercise with intensity guidance, the app estimates HR from the pulse obtained using the smartphone’s built-in camera and compares the estimated HR with the THZ. Based on this comparison, the app adjusts the exercise intensity to shift the patient’s HR to the THZ during exercise. In the post-exercise period, the app manages the ratio of the estimated HR to the THZ and provides a questionnaire on factors such as chest pain, shortness of breath, and leg pain during exercise, as objective and subjective evaluation indicators. As a key issue, HR estimation upon signal corruption due to motion artifacts is also considered. Through the smartphone-based CRP, we estimated the HR accuracy as mean absolute error and root mean squared error of 6.16 and 4.30bpm, respectively, with signal corruption due to motion artifacts being detected by combining the turning point ratio and kurtosis.

A TiO 2 -Coated Reflective Layer Enhances the Sensitivity of a CsI:Tl Scintillator for X-ray Imaging Sensors

Columnar-structured cesium iodide (CsI) scintillators doped with thallium (Tl) are frequently used as x-ray converters in medical and industrial imaging. In this study we investigated the imaging characteristics of CsI:Tl films with various reflective layers-aluminum (Al), chromium (Cr), and titanium dioxide (

Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

TiO_2

Multiple switching light sources based motion artifacts reduction in reflectance photoplethysmography

) powder-coated on glass substrates. We used two effusion-cell sources in a thermal evaporator system to fabricate CsI:Tl films on substrates. The scintillators were observed via scanning electron microscopy (SEM), and scintillation characteristics were evaluated on the basis of the emission spectrum, light output, light response to x-ray dose, modulation transfer function (MTF), and x-ray images. Compared to control films without a reflective layer, CsI:Tl films with reflective layers showed better sensitivity and light collection efficiency, and the film with a

Heart activity monitoring using 3D hologram based on smartphone

TiO_2

Analysis of Statistical Methods for Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

reflective layer showed the best properties.

Simplified 3D Hologram Heart Activity Monitoring Using a Smartphone

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n =23), the MIT-BIH Normal Sinus Rhythm (n =18), the BIDMC Congestive Heart Failure (n =13) and the Congestive Heart Failure RRI databases (n =25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n =42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

Real-time heart activity monitoring with optical illusion using a smartphone

We proposed a technique to eliminate motion artifacts by subtracting the two green signals with different light intensity from single green LED. We used green LED light source with a photodetector to obtain the PPG signals from the wrist. We showed that the signal subtraction with different light intensity from the same light source reduced motion artifacts, and the clean PPG signal was remained. Performance comparison was carried out in five different scenarios: stationary state, fingers movement, gripping, up-down vertically bending and left-right horizontally swinging conditions. As expected, we obtained suitable clean PPG signals in all conditions using our proposed technique.We proposed a technique to eliminate motion artifacts by subtracting the two green signals with different light intensity from single green LED. We used green LED light source with a photodetector to obtain the PPG signals from the wrist. We showed that the signal subtraction with different light intensity from the same light source reduced motion artifacts, and the clean PPG signal was remained. Performance comparison was carried out in five different scenarios: stationary state, fingers movement, gripping, up-down vertically bending and left-right horizontally swinging conditions. As expected, we obtained suitable clean PPG signals in all conditions using our proposed technique.