• Journal of IKEEE
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• v.21 no.4
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• pp.397-403
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• 2017

In the modern society, psychological diseases and impulsive crimes due to stress are occurring. In order to reduce the stress, the existing treatment methods consisted of continuous visit counseling to determine the psychological state and prescribe medication or psychotherapy. Although this face-to-face counseling method is effective, it takes much time to determine the state of the patient, and there is a problem of treatment efficiency that is difficult to be continuously managed depending on the individual situation. In this paper, we propose an artificial intelligence emotion management system that emotions of user monitor in real time and induced to a table state. The system measures multiple bio-signals based on the PPG and the GSR sensors, preprocesses the data into appropriate data types, and classifies four typical emotional states such as pleasure, relax, sadness, and horror through the SVM algorithm. We verify that the emotion of the user is guided to a stable state by providing a real-time emotion management service when the classification result is judged to be a negative state such as sadness or fear through experiments.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.275-283
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• 2001

This study was performed to extract and analyze the biosignals to find the relationship between the level of anesthesia and the variations of physiological parameters during epidural anesthesia. Seven male and twenty female patients(ages from 45 to 70 years old) were participated for the experiment, and ECGs, PPGs, SKTs, SCRs were obtained during anesthesia. As results, the HF/LF ratios of HRV were decreased after the injection anesthetics. For skin temperatures, values measured from the palm was reduced and the temperatures from four channels, measured from armpit through the right side of the body, were increased. SCRs were decreased for all channels after the injection of anesthetics. However the heart rate and PPGs showed no significant changes. It was concluded that the injection of anesthetics result the changes in biosignals, and it could be explained by the degree of the sympathetic and/or parasympathetic nerve activities. Results of this study could provide the valuable information for the estimation of level for the spinal and general anesthesia, and could be extended to the development of a system which could quantify the level of anesthesia.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.217-225
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• 2004

In this paper, a new automatic classification method for the normal EEC and schizophrenia EEC using hidden Markov model(HMM) is proposed. We used the feature parameters which are the variance for statistical stationary interval of the EEC and power spectrum ratio of the alpha, beta, and theta wave. The results were shown that high classification accuracy of 90.9％ in the case of normal person, and 90.5％ in the case of schizophrenia patient. It seems that proposed classification system is more efficient than the system using complicate signal processing process. Hence, the proposed method can be used at analysis and classification for complicated biosignal such as EEC and is expected to give considerable assistance to clinical diagnosis.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.54-62
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• 2024

The emergence and resurgence of novel respiratory infectious diseases since the turn of the millennium, including SARS, H1N1 flu, MERS, and COVID-19, have posed a significant global health threat. Efforts to combat these threats have involved various approaches, however, continued research and development are crucial to prepare for the possibility of emerging viruses and viral variants. Direct detection methods for viral pathogens include molecular diagnostic techniques and immunodiagnostic methods, while indirect diagnostic methods involve detecting changes in the condition of infected patients through imaging diagnostics, gas analysis, and biosignal measurement. Molecular diagnostic techniques, utilizing advanced technologies such as gene editing, are being developed to enable faster detection than traditional PCR methods, and research is underway to improve the efficiency of diagnostic devices. Diagnostic technologies for infectious diseases continue to evolve, and several key trends are expected to emerge in the future. Automation will facilitate widespread adoption of rapid and accurate diagnostics, portable diagnostic devices will enable immediate on-site diagnosis by healthcare professionals, and advancements in AI-based deep learning diagnostic models will enhance diagnostic accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2170-2178
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• 2014

In this paper, electroencephalographic (EEG) signal of one among subjects measured biosignal with visual evoked stimuli inducing the concentration was analyzed to detect the changes in the attention status during attention task fulfillment from January to February, 2011. The independent component analysis (ICA) was applied to EEG signals to isolate the attention related innate source signal within the brain and Electroculogram (EOG) artifact from measured EEG signals at the scalp. The consecutive accumulation of short time Fourier transformed (STFT) attention source signal with excluded EOG artifact can enhance the regular depiction of EPOCH graph and spectral color map representing time-varying pattern. The extracted attention indices associated with somatosensory rhythm (SMR: 12-15 Hz), and theta wave (4-7 Hz) increase marginally over time. Throughout experimental observation, the ICA with STFT can be used for the assessment of participants' status of attention.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.369-376
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• 2007

With the convergence of ubiquitous networking and medical technologies, ubiquitous healthcare(U-Healthcare) service has come in our life, which enables a patient to receive medical services at anytime and anywhere. In the u-Healthcare environment, intelligent real-time biosignal aquisition/analysis techniques are inevitable. In this study, we propose a motion artifact cancelation method in portable photoplethysmography(PPG) signal aquisition using an accelerometer and an adaptive filter. A preliminary experiment represented that the component of the pedestrian motion artifact can be found under 5Hz in the spectral analysis. Therefore, we collected PPG signals under both simulated conditions with a motor that generates circular motion with uniform velocity (from 1 to 5Hz) and a real walking condition. We then reduced the motion artifact using a recursive least square adaptive filter which takes the accelerometer output as a noise reference. The results showed that the adaptive filter can remove the motion artifact effectively and recover peak points in PPG signals, which represents our method can be useful to detect heart rate in real walking condition.

• Journal of Oriental Neuropsychiatry
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• v.31 no.2
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• pp.89-99
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• 2020

Objectives: The purpose of this study was to identify the needs of bio-signal devices for the diagnosis, assessment, and analysis of neurocognitive disorder in Korean medicine (KM) hospitals and clinics. Methods: A questionnaire was developed to survey the current status of medical device use, and diagnosis and interventions for patients with cognitive disorders in KM hospitals and clinics. November 11~December 2, 2019, 114 responses (71.9% completed) were collected by internet-based questionnaires from the members of the Korean society of Oriental Neuropsychiatry. Results: The clinical requests were in the descending order of hematology analyzer, ultrasound imaging system, and electroencephalography among the 15 most commonly used devices of which research would support for their clinical usability. The biosignal-based devices showed the highest research demands for patients with mild cognitive impairment rather than more severe stages of cognitive impairment. Prevention rather than diagnosis, or several treatment regimens was the strongest clinical area of the KM for patients with neurodegenerative cognitive impairment. Many responded that five to 10 minutes of test duration and 20,000 won to 30,000 won of cost would be appropriated for a new device to be developed. Conclusions: There were strong demands for the development of bio-signal devices for neurocognitive disorders among the KM doctors. Specifically, it showed high needs for the technology that can be used in the prevention area of cognitive disorders. Additionally, new medical devices to assess cognitive functions and to obtain KM pattern-related information were the high needs.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.113-118
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• 2024

Counseling is not merely a conversation it is a critical meeting aimed at solving problems. For counseling to be effective, the client must be truthful and candid. However, it is not uncommon for clients to provide false answers or remain silent during counseling sessions. Such passive behavior can diminish the quality of counseling. Therefore, this paper presents the design of a counseling support system that utilizes multimodal biometric signal measurement and analysis. The proposed system analyzes the client's EEG, GSR, and breathing patterns during counseling sessions, enabling counselors to accurately assess the emotional and physical state of the client and devise appropriate counseling strategies. This system enhances the efficiency of counseling, allows for rapid response to psychological changes, and incorporates technologies to enable personalized counseling and maximize the effectiveness of sessions. Future research will focus on expanding the system's application range and improving the user interface to develop a more effective and user-friendly tool.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.7-14
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• 2006

Most researches about rumble strips have Presented only the before-and-after analysis of the accidents. So, Researchers have not dealt with the estimation of rumble strip's effectiveness on the driver's alertness. In this study. the effectiveness of the rumble strips on the driver's alertness was estimated by measuring the bio-signal transmitted from the driver. The bio-signal acquired for this experiments were theta wave in central lobe. The experimental results revealed that the theta waves as measured form the drivers's head while in the rumble strip section differed from those while in non-rumbled section; 74 percent decrease in theta wave value, respectively. This fact finding could mean that the driver's alertness increased from 74 percent while in the rumble strip section of the road. In all five trials of driving experiments on the rumble strip section, all the drivers showed the best alertness as measured by the theta waves in the first driving trial.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.113-123
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• 2020

Obstructive sleep apnea (OSA) among sleep disorders is one of relatively common diseases. Patients can be checked for the disease through sleep polysomnography. However, as far as he diagnosis of OSA using polysomnography (PSG) is concerned, many practical problems such as an increasing number of patients, expensive testing cost, discomfort during examination, and the limited number of people for testing have been pointed out. Accordingly, for the purpose of substituting PSG researchers have been actively conducting studies on OSA diagnosis based on machine learning using bio signals. In this regard, we review a rich body of existing OSA diagnosis studies applying machine learning techniques based on bio-signal data. As a result, this paper presents a novel taxonomy of the reviewed studies and provides their comprehensive comparative analysis results. Also, we reveal various limitations of the studies using the bio signals and suggest several improvements about utilization of the used machine learning methods. Finally, this paper presents future research topics related to the application of machine learning techniques using bio signals.