• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.439-448
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• 2001

This paper considers the acoustical characteristics of the closing click sounds of the mechanical valves employed in an implantable biventricular assist device (BYAD) and their re1evance to the Physical states of the valved. Bj rk Shiley Convexo Concave tilting disk valve was chosen for the study and acoustic measurement was made for the BYAD operated in a mock circulatory system as well as implanted in an animal (sheep). In the BYAD operated in the mock circulatory system. three different states of the valve were examined, ie. normal. mechanically damaged. pseudo-thrombus attached. Microphone measurement for the BVAD implanted in the animal was carried out for five days at a regular time interval from one day after implantation. Characteristic spectrum of the sound from the valve was estimated using Multiple Signal Classification (MUSIC) in which the optimal order was determined according to Bayesian Information Criterion (BIC) . It was observed that the mechanical damage of the valve resulted in changes of the structure of the acoustic spectrum. In contrast. the thrombus formed on the valve did not change much the basic structure of the spectrum but brought about altering the spectral Peak frequencies and energies. Maximum spectral Peak (MSP) with the greatest energy was seen at 2 kHz for the normal valve and it was shifted to 3 kHz for the calve attaching the Pseudo-thrombus. Unlike the normal valve, strong spectral Peak appeared around 7 kHz in the sound from the valve mechanically damaged. In the case of the BYAD implanted in the animal. as the thrombus grew, acoustic energy was reduced relatively more in the low frequency components (〈 2 kHz) and the frequencies of the 1st, 2nd and 3rd MSP were increased little. The thrombus formation would result in reduction in both the variability of the 1st, 2nd and 3rd MSP and the value of the BIC optimal order.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.131-134
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• 1999

This paper considers the acoustical characteristics of the mechanical valve employed in the Korean type Artificial Heart. $Bj\"{o}rk-Shiley$ tilting disc valve was chosen for the study and acoustic measurements were performed for the artificial heart operated in a mock circulation system as well as implanted to an animal as a Bi Ventricular Assist Device (BVAD). In the mock system, three different conditions of the valve were examined which were normal, damaged (torn off), pseudothrombus attached. Microphone measurements for the BVAD were carried out at a regular time interval for 5 days after the implantation operation. Of the recorded acoustic emissions from the artificial heart, click sounds mainly originated from the valves were further analyzed using Multiple Signal Classification (MUSIC) for estimating their spectral properties. It was shown that the spectral peaks below 4 kHz and the optimal order number for MUSIC, equivalent to the number of the spectral component, might be the key parameters which were highly correlated to the physiological states of the valve like the mechanical damage of the valve or the formation of thrombus on the valves.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.177-182
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• 2015

The activation of prefrontal cortex of brain during some mental tasks like mental arithmetic induce has been studied using hemodynamic imaging modalities. In this study, we focused on the differentiation of activated area in local prefrontal brain caused by the different mental activities as well as evaluating the classification accuracy of in-house fNIRS system. The study preliminarily validated the device including the signal quality and tightness of contact between detectors and prefrontal area. Experimental results of mental tasks on 5 subjects showed the subject dependent tendencies in correlated prefrontal activation and the area of highest accuracy.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.90-97
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• 1999

Recently, there have been many researches to automate processing and analysing image data in medical field, due to the advance of image processing techniques, the fast communication network and high performance hardware. In this paper, we design and implement the system based on the multi-layer neural network model to be able to analyze, differentiate and count blood cells in the peripheral blood image. To do these, we segment red and white-blood cell in blood image acquired from microscope with CCD(Charge-coupled device) camera and then apply the various feature extraction algorithms to classify. In addition to, we reduce multi-variate feature number using PCA(Principle Component Analysis) to construct more efficient classifier. So, in this paper, we are sure that the proposed system can be applied to a pathological guided system.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• Journal of Biomedical Engineering Research
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• v.34 no.3
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• pp.141-147
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• 2013

Ultrasonic shears is currently in wide use as an energy device for minimal invasive surgery. There is an advantage of minimizing the carbonization behavior of the tissue due to the vibrational energy transfer system of the transducer by applying a piezoelectric ceramic. However, the vibrational energy transfer system has a pitfall in energy consumption. When the movement of the forceps is interrupted by the tissue, the horn which transfers the vibrational energy of the transducer will be affected. A study was performed to recognize different tissues by measuring the impedance of the transducer of the ultrasonic shears in order to find the factor of energy consumption according to the tissue. In the first stage of the study, the voltage and current of the transducer connecting portion were measured, along with the phase changes. Subsequently, in the second stage, the impedance of the transducer was directly measured. In the final stage, using the handpiece, we grasped the tissue and observed the impedance differences appeared in the transducer To verify the proposed tissue distinguishing method, we used the handpiece to apply a force between 5N and 10N to pork while increasing the value of the impedance of the transducer from 400 ${\Omega}$.. It was found that fat and skin tissue, tendon, liver and protein all have different impedance values of 420 ${\Omega}$, 490 ${\Omega}$, 530 ${\Omega}$, and 580 ${\Omega}$, respectively. Thus, the impedance value can be used to distinguish the type of tissues grasped by the forceps. In the future study, this relationship will be used to improve the energy efficiency of ultrasonic shears.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.361-368
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• 2022

The purpose of this study was to develop a hand rehabilitation training system for hemiplegic patients. We also tried to find out five hand postures (WF: Wrist Flexion, WE: Wrist Extension, BG: Ball Grip, HG: Hook Grip, RE: Rest) in real-time using multi-channel EMG-based deep learning. We performed a pre-processing method that converts to Spider Chart image data for the classification of hand movement from five test subjects (total 1,500 data sets) using Convolution Neural Networks (CNN) deep learning with an 8-channel armband. As a result of this study, the recognition accuracy was 92% for WF, 94% for WE, 76% for BG, 82% for HG, and 88% for RE. Also, ten physical therapists participated for the usability evaluation. The questionnaire consisted of 7 items of acceptance, interest, and satisfaction, and the mean and standard deviation were calculated by dividing each into a 5-point scale. As a result, high scores were obtained in immersion and interest in game (4.6±0.43), convenience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Conformity of intention for treatment (3.90±0.49) was relatively low. This is thought to be because the game play may be difficult depending on the degree of spasticity of the hemiplegic patient, and compensation may occur in patient with weakened target muscles. Therefore, it is necessary to develop a rehabilitation program suitable for the degree of disability of the patient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.805-813
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• 2018

The research was conducted with the purpose of providing effective safety and quality control system for assistive products for handicapped those are used extensively. Assistive products couldn't be classified independently due to collision with the act of medical device and lack in legal basis. The issues about safety and quality have been solved by other legal frames on a case by case basis. We couldn't find any abroad case of independent safety and quality control policy. For the practical solution, this article suggested hybrid classification system mixed with existing policies. Each classified branches are allocated to the appropriate policy of safety and quality control so those are ease of understanding and prospect. And also a delicacy process was suggested not to leave off any assistive products. Through these suggests of the improvement it is expected that blind areas of safety and quality control for assistive products for handicapped could be solved and identity of assistive products could be established to provide product safety for handicapped and boost relevant industries.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.