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Subject: search.filters.subject.Photoplethysmography

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    New pulse oximetry detection based on the light absorbance ratio as determined from amplitude modulation indexes in the time and frequency domains
    (2022-05-01)
    Kainan, Pattana
    ;
    Sinchai, Ananta
    ;
    Tuwanut, Panwit
    ;
    Wardkein, Paramote
    The Pandemic COVID-19 situation, a pulse Oximetry is significant to detect a varying blood oxygen saturation of a patient who needed the device to operate with continuous, rapid, high accuracy, and immune of moving artifacts. In this article, three main schemes for low-complexity pulse oximetry detection are proposed. In the first scheme, the light absorbance ratio (R) is obtained by separating the red and infrared photoplethysmography (PPG) amplitude modulation (AM) signals from the frequency-division multiplexing (FDM) signal with two different bandpass filters (BPFs), determining the ratio of modulation index of red and infrared PPG AM signals. In the second scheme, the output PPG AM signals for the red and infrared light wavelengths from the BPFs are transformed into the frequency domain such that the AC components of both PPG AM signals are the magnitudes of the highest peaks in their respective sidebands, while the DC components are the magnitude of their carrier frequencies; then, the AC/DC ratio of the red PPG AM signal is divided by the AC/DC ratio of the infrared PPG AM signal is R. In the last scheme, the FDM signal is transformed into the frequency domain without being passed through any BPF, and R is obtained in the same way as in the same second scheme. Experimental results obtained by using the first scheme have an average error of about 0.7138%, for the second and the last scheme have an average error of about 1%, and all the methods agree with the corresponding mathematical model.
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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
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    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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    Noninvasive portable hemoglobin concentration monitoring system using optical sensor for anemia disease
    (2021-06-01)
    Pintavirooj, Chuchart
    ;
    Ni, Baorong
    ;
    Chatkobkool, Chaiwat
    ;
    Pinijkij, Kittitorn
    Anemia is a condition in which red blood cells are not able to carry adequate oxygen to the body’s tissues, and is widely found in nearly a quarter of the world population. The typical method to screen for the iron-deficiency anemia, which is the major anemia found in the world, is to implement a blood test called a complete blood count (CBC). However, even though this test gives a highly accurate result, it requires an invasive blood drawing and lab analyzing which could potentially cause physical pain, high risk of infection and take a long time to analyze. Therefore, this research presents an alternative method using an optical technique to measure hemoglobin concentration, which is the common indicator for diagnosing anemia. The light absorbance of the oxyhemoglobin at the wavelength of 660 nm and the deoxyhemoglobin at the wavelength of 880 nm were measured using the MAX30100 sensor. These wavelengths of light are obtained from red and infrared (IR) LEDs. The concept is based on the different absorption coefficients of blood at different electromagnetic wavelengths. This fact is used to indirectly calculate the hemoglobin concentration of blood through the modified Beer–Lambert law. Moreover, the result has been further converted to absolute hemoglobin concentration using a calibration curve derived from the cyanmethemoglobin test, which is the regular method for hemoglobin determination. Besides, the android application was also provided which can wirelessly record or monitor the data. The experiment shows that an accuracy of 90.9% can be achieved by our proposed noninvasive method. Therefore, the noninvasive portable hemoglobin concentration monitoring by the optical sensor has an acceptable result when compared with the invasive method, with less pain and lower risk of infection, as well as shorter processing time.
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    Comparison of Heart Rate statistical parameters from Photoplethysmographic signal in resting and exercise conditions
    (2015-08-17)
    Sengthipphany, T.
    ;
    Tretriluxana, S.
    ;
    Chitsakul, K.
    The aim of this article is to investigate the possibility of computing Heart Rate Variability (HRV) indices from the finger Photoplethysmogram. To indicate that whether Pulse Rate Variability (PRV) from PPG signal can be used as an alternative to Heart Rate Variability (HRV) from ECG signal. The Photoplethsmographic (PPG) and Electrocardiographic (ECG) signals from 33 participants were recorded simultaneoussly during resting and exercise conditions. The peaks of PPG and R-waves of ECG were detected and reconstructed to the Peak-to-Peak interval (PPI) and R-to-R interval (RRI) waveforms respectively. In both conditions, the mean and standard deviation (SDNN) of the intervals over 5 minutes were computed from the two waveforms. Results from cross correlation between PPI and RRI show that the average correlation coefficients (r) are higher in resting than the 'r' in exercise. In regression analysis of the statistical parameters between RRI and PPI, the determination coefficients (R<sup>2</sup>) of the means are close to one in both conditions, whereas the R<sup>2</sup> of SDNN in exercise is lower than the one in resting. This finding suggests that the HRV indices can be evaluated from PPG with reliability during the rest.