Abstract:Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources… Show more
“…As an example of using pill images, if a neural network is trained with the data obtained from the pill filming system, it may be possible to develop a model that can determine the type of pill with only one picture. Abundant data facilitates high judgment accuracy of neural networks, so we initially set the pill filming system to generate 300 images [ 16 ]. However, the user can set the amount of data collected and the execution time through the GUI.…”
Objectives: Since the easiest way to identify pills and obtain information about them is to distinguish them visually, many studies on image processing technology exist. However, no automatic system for generating pill image data has yet been developed. Therefore, we propose a system for automatically generating image data by taking pictures of pills from various angles. This system is referred to as the pill filming system in this paper.Methods: We designed the pill filming system to have three components: structure, controller, and a graphical user interface (GUI). This system was manufactured with black polylactic acid using a 3D printer for lightweight and easy manufacturing. The mainboard controls data storage, and the entire process is managed through the GUI. After one reciprocating movement of the seesaw, the web camera at the top shoots the target pill on the stage. This image is then saved in a specific directory on the mainboard.Results: The pill filming system completes its workflow after generating 300 pill images. The total time to collect data per pill takes 21 minutes and 25 seconds. The generated image size is 1280 × 960 pixels, the horizontal and vertical resolutions are both 96 DPI (dot per inch), and the file extension is .jpg.Conclusions: This paper proposes a system that can automatically generate pill image data from various angles. The pill observation data from various angles include many cases. In addition, the data collected in the same controlled environment have a uniform background, making it easy to process the images. Large quantities of high-quality data from the pill filming system can contribute to various studies using pill images.
“…As an example of using pill images, if a neural network is trained with the data obtained from the pill filming system, it may be possible to develop a model that can determine the type of pill with only one picture. Abundant data facilitates high judgment accuracy of neural networks, so we initially set the pill filming system to generate 300 images [ 16 ]. However, the user can set the amount of data collected and the execution time through the GUI.…”
Objectives: Since the easiest way to identify pills and obtain information about them is to distinguish them visually, many studies on image processing technology exist. However, no automatic system for generating pill image data has yet been developed. Therefore, we propose a system for automatically generating image data by taking pictures of pills from various angles. This system is referred to as the pill filming system in this paper.Methods: We designed the pill filming system to have three components: structure, controller, and a graphical user interface (GUI). This system was manufactured with black polylactic acid using a 3D printer for lightweight and easy manufacturing. The mainboard controls data storage, and the entire process is managed through the GUI. After one reciprocating movement of the seesaw, the web camera at the top shoots the target pill on the stage. This image is then saved in a specific directory on the mainboard.Results: The pill filming system completes its workflow after generating 300 pill images. The total time to collect data per pill takes 21 minutes and 25 seconds. The generated image size is 1280 × 960 pixels, the horizontal and vertical resolutions are both 96 DPI (dot per inch), and the file extension is .jpg.Conclusions: This paper proposes a system that can automatically generate pill image data from various angles. The pill observation data from various angles include many cases. In addition, the data collected in the same controlled environment have a uniform background, making it easy to process the images. Large quantities of high-quality data from the pill filming system can contribute to various studies using pill images.
“…Each DNN filter consists of one input layer, two hidden layers, and one output layer as shown in Fig. 1 b [ 13 – 16 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…If 80 is determined as a reliable region, and 80 is determined as a noisy region. This value determines the first normal/abnormal judgement of the signal [ 13 – 16 ].…”
Section: Methodsmentioning
confidence: 99%
“…To evaluate the reliability of the signal, the DNN filter-based system proposed in previous studies was applied [ 13 – 16 ]. The DNN filter technique can extract valid indicators (six singular points) from the PPG signal by applying a neural network in the form of a digital filter.…”
Telemedicine data are measured directly by untrained patients, which may cause problems in data reliability. Many deep learning-based studies have been conducted to improve the quality of measurement data. However, they could not provide an accurate basis for judgment. Therefore, this study proposed a deep neural network filter-based reliability evaluation system that could present an accurate basis for judgment and verified its reliability by evaluating photoplethysmography signal and change in data quality according to judgment criteria through clinical trials. In the results, the deviation of 3% or more when the oxygen saturation was judged as normal according to each criterion was 0.3% and 0.82% for criteria 1 and 2, respectively, which was very low compared to the abnormal judgment (3.86%). The deviation of diastolic blood pressure (≥ 10 mmHg) according to criterion 3 was reduced by about 4% in the normal judgment compared to the abnormal. In addition, when multiple judgment conditions were satisfied, abnormal data were better discriminated than when only one criterion was satisfied. Therefore, the basis for judging abnormal data can be presented with the system proposed in this study, and the quality of telemedicine data can be improved according to the judgment result.
“…Moreover, human pulsatile blood ow includes pulses re ected from peripheral vessels in addition to the heart's pulsation, and with the addition of pulsatile blood ow by the p-ECMO, measuring the heart's pulse rate and controlling CP using a simple algorithm becomes di cult [22]. Recently, lter-type neural networks (f-NNs) have been developed that can differentiate, in real time, between the heart's pulse and re ected waves in photoplethysmogram (PPG) waveforms [23][24][25]. As PPG waveforms are similar to those of BP, f-NNs can be used to distinguish between the pulsations of the heart and p-ECMO.…”
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate detection of heartbeats within blood pressure (BP) waveform data becomes imperative, especially in situations where measuring electrocardiograms (ECGs) are difficult or impractical. In this study, a cumulative algorithm incorporating filter-type neural networks was developed to distinguish heartbeats from other pulse signals generated by the p-ECMO, reflections, or motion artifacts in the BP data. A control system was implemented using the cumulative algorithm that detects the heart rate (HR) and maintains a proper interval between the p-ECMO's pulses and heart beats, thereby achieving CP. To ensure precise circulatory support control, the p-ECMO setup was connected to a mock circulation system, with the human BP waveforms being replicated using a heart model. The algorithm could maintain CP perfectly when the HR remained constant; however, owing to a 0.48-s delay from the HR detection to CP control, the success rate of the CP control decreases when a sudden increase in the HR occurred. In fact, when the HR varied by ± 5 bpm every minute, the CP success rate dropped to 78.62%, however this was still higher compared to the 25.75% success rate achieved when no control was applied.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
