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IoT-based Architecture and Implementation for Automatic Shock Treatment
이남화,Minsu Jeong,김영재,Jisoo Shin,조인휘,Sanghoon Jeon,고벽성 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.7
The Internet of Things (IoT) is being used in a wide variety of fields due to the recent 4th industrial revolution. In particular, research is being conducted that combines IoT with the medical field such as telemedicine. Among them, the field of shock detection is a big issue in the medical field because the causes of shock are diverse, treatments are very complex, and require a high level of medical knowledge and experience. The transmission of infectious diseases is common when treating critically ill patients, especially patients with shock. Thus, to effectively care for shock patients, we propose an architecture that continuously monitors the patient’s condition, and automatically recommends a drug injection treatment according to the patient’s shock condition. The patient’s hemodynamic information is continuously monitored, and the patient’s shock generation information is recorded periodically. With the recorded patient information, the patient’s condition is determined and automatically injected with necessary medication. The medical team can find out whether the patient’s condition has improved by checking the recorded information through web applications. The study can help relieve the shortage of medical personnel and help prevent transmission of infectious disease in medical staff. We look forward to playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult shocks.
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류상욱,이남화,이연수,조인휘,민경육,김택수 한국반도체디스플레이기술학회 2020 반도체디스플레이기술학회지 Vol.19 No.2
In this paper, the new architecture of seizure prediction using CNN and LSTM and DWT was presented. In the proposed architecture, EEG data was labeled into a preictal and interictal section, and DWT was adopted to the preprocessing process to apply the characteristics of the time and frequency domain of the processed EEG signal. Also, CNN was applied to extract the spatial characteristics of each electrode used for EEG measurement, and LSTM neural network was applied to verify the logical order of the preictal section. The learning of the proposed architecture utilizes the CHB-MIT Scalp EEG dataset, and the sliding window technique is applied to balance the dataset between the number of interictal sections and the number of preictal sections. As a result of the simulation of the proposed architecture, a sensitivity of 81.22% and an FPR of 0.174 were obtained.
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Ensemble Deep Learning Model using Random Forest for Patient Shock Detection
Minsu Jeong,이남화,Byuk Sung Ko,Inwhee Joe 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4
Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient’s shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.
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