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  • Changes in the Perception of Postoperative Delirium Before and After a Simulated Experience of Postoperative Delirium in Nursing Students

    Abstract: The purpose of this study is to use Unity to recreate the hallucinations and auditory
    hallucinations experienced by patients who develop delirium, and to use VR to reveal the changes in perception of delirium before and after the simulated experience of delirium to nursing college students. We used VR to recreate the ICU at night and created a video of a simulated postoperative delirium experience. The duration is 12 minutes. We set up scenes of cockroaches appearing on the ceiling, the ceiling closing in on them, people in protective clothing, and soldiers attacking at two-minute intervals. Oculus Quest was used as the head-mounted display (HMD) for viewing the VR images. The target participants were 17 students in the second to the fourth year of nursing college. The target students were asked to answer two questions before and after the viewing. The interview content was analyzed by comparing the differences in speaking time, amount of speech and content before and after the VR viewing for each student and by text mining. The results showed that using VR to simulate postoperative delirium can lead to a change in perception from understanding the inner life of patients with postoperative delirium.

  • Geometric Shape Statistical Analysis of Tibial Plafond with Ankle Osteoarthritis

    Abstract: The etiology of ankle osteoarthritis is not enough elucidated, and similar to hip and knee
    osteoarthritis, the ankle osteoarthritis caused cartilage loss and pain in daily life. However, the ankle osteoarthritis has a worse prognosis than the hip or the knee osteoarthritis. Considering the ankle osteoarthritis skeletal structure is important in selecting the appropriate surgery for the patient. Selecting the appropriate surgery will lead to an improved prognosis. X-ray images and Computed Tomography (CT) scan images are usually used to classify ankle osteoarthritis, but the evaluation of 3D bone structure is difficult and the classification based on two-dimensional measurements of X-ray and CT images may vary among medical doctors. The purpose of this study is to investigate the three-dimensional geometric deformation characteristics of tibial plafond by using statistical analysis. Deformation characteristics were found in high severity ankle osteoarthritis compared with mild group. In particular, there was a statistically significant difference in the varus deformity of the articular facet of medial malleolus and tibial plafond between stage 3B and mild group (p < 0.006), and the hyperostosis of the medial malleolus between stage 4 and mild group (p < 0.002). These results suggest that patients with severe ankle osteoarthritis have common deformity characteristics in the tibial plafond. On the contrary, there was no significant difference in the deformity of the edge of anterior and posterior tibial plafond. Such phenomena suggest that the edge of anterior and posterior tibial plafond deforms regardless of ankle osteoarthritis. This study contributes to the scientific advancement of ankle osteoarthritis surgery.

  • Audio-based Wearable Contexts Recognition System for Apnea Detection

    Abstract: Apnea or Sleep Apnea Syndrome is a condition when a person unconsciously stops
    breathing during a sleeping state for longer than a certain time. Long-term and multiple apnea events induce various impairments. However, apnea detection in hospitals is an intensive and complicated procedure and this causes highly undiagnosed and low awareness of the disease. Existing wearable devices for apnea detections mostly used heartbeat signal patterns and SpO2 levels to detect the disease, however since apnea is a respiratory impairment, it is believed that using a breathing pattern is the most straightforward approach in apnea detection. Several recent studies investigated that swallowing frequency during sleep can increase along with the apnea severity. However, the number of wearable devices using swallowing to detect apnea is very limited. Thus, this study proposes a wearable system to recognize human contexts such as breathing, heartbeat pattern, and swallowing using an audio sensor. Experiments were conducted to compare and obtain the most suitable parameters for the system such as window sizes, types of audio feature values, and classification algorithms. The prototype of the device was built and able to detect breathing, swallowing, heartbeat, oral sounds, and body movement. The result shows the best accuracy of 76.9% using 1s window size and Mel’s Frequency Cepstral Coefficient (MFCC) features in contact microphone data.

  • Evaluation of Markerless Gait Analysis Method Including Out of Camera Plane Rotate Motion During Gait

    Abstract: A RGB camera gait analysis system that does not require markers, large space, and
    preparation can provide valuable information for effective treatment decisions in clinical settings. In this paper, we propose a simple markerless gait analysis method that can measure even if the rotation angle of the foot changes. The proposed method combines OpenPose (OP) and IMU measurement data using a complementary filter as a sensor fusion method to improve the measurement accuracy of the ankle joint angle, which is predicted to be less accurate for gait with a large foot rotation angle. Nine healthy adult males walked at a self-selected comfortable speed in two different foot-progression angle gait conditions. Spatio-temporal parameters and lower limb joint angles in the two gait conditions were measured. The mean absolute error (MAE) and the coefficient of cross-correlation (CCC) were calculated to evaluate the accuracy. The spatio-temporal parameters measured by the proposed method had low MAE compared with a conventional markerless method. The similarity between the changes in the angles of the hip and knee joints and the changes in the angles measured by a three-dimensional motion capture system was found to be very strongly correlated (CCC > 0.7). The MAE of the hip and
    knee joint angles measured by the proposed method was small compared with a conventional markerless method. In particular, the proposed method was able to improve the measurement accuracy of the ankle angle by using two IMUs. The experimental results suggest that the proposed method can be used for simple and accurate measurement even when the rotation angle of the foot changes. Although the proposed method has some limitations, it has great potential as a simple and reliable gait analysis system in the clinical field.

  • Effect of the random forest with recursive feature elimination for breast cancer classification using a WDBC dataset

    Abstract:  A breast cancer is the most dangerous disease of the death cause among aged 40-55 women. We need a computer aided diagnosis system for breast cancer classification. In the previous study, the random forest which is known as an ensemble learning method was reported to be one of promising classifiers for classifying breast cancers using a Wisconsin Diagnostic Breast Cancer(WDBC) dataset. This paper presents the effect of the random forest with a recursive feature elimination for breast cancer classification on the WDBC dataset, compared to the state of the art ensemble learning techniques, such as XGBoost and LightGBM.

     

  • The Comparison of Two-Classes Basic Emotion Classification Methods Using a Single Heart rate change Parameter

    Abstract:  Emotion is a multifaceted phenomenon that plays a critical role in enhancing one’s quality of life by influencing motivation, perception, cognition, creativity, empathy, education, and decision-making. Additionally, negative emotions such as anger, shame, and anxiety are frequently triggered by stress, and the term destructive and threatening is used to indicate a connection between them. As a result, research into emotion recognition remains a critical issue at the moment. This study enrolled fifteen male university students. The heart rate was determined using a fingertip photoplethysmograph (PPG). The International Affective Picture System (IAPS) was used in this study to facilitate emotion changes. We used the Self-Assessment Manikin (SAM) to evaluate the subject’s emotions during the psychological assessment. As a pre-processing method, the FIR Band Pass Filter was established, and a single parameter called Heart rate change (HRC) was extracted from a PPG recording. Rather than employing complex classification techniques, we used binary classifiers such as logistic regression, Naïve Bayes, and Support Vector Machine (SVM) to distinguish between negative and positive emotions. We discovered that Naïve Bayes could provide greater than 50% accuracy and Area Under Curve (AUC) compared to the others using data from 30%, 40%, and 50% test sizes, respectively, particularly happiness (positive emotion) and anger (negative emotion). We concluded that the HRC as a single parameter could be considered the fundamental emotion classifier, though further research is necessary.

    Keywords: Emotions; Binary Classifier; SAM; Photoplethysmograph

  • Classification of Breast Pathology based on Transfer Learning by MobileNet

    Abstract:  Breast cancer is the most common cancer among women worldwide. By using artificial intelligent technique, the efficiency of cancer diagnosis can be effectively improved. However, the computer-aided diagnosis (CAD) has problems such as long training time for large-resolution pathological images and insufficient data that can be marked for training. In this article, a transfer learning model for pathological diagnosis of breast cancer is developed to overcome these problems. MobileNet was adopted to train breast pathology images under four different resolutions (40X, 100X, 200X, 400X). A transfer learning framework was established to distinguish benign and malignant breast pathologies and eight subtypes. The accuracy of the two-class model at the best magnification (200X) can reach 91.24%, and the average accuracy is 89.31%. At the same time, the multi-classification model of eight subtypes of pathological slices also achieved quite satisfactory results. It is show that the presented transfer learning framework has great potential in exploring the CAD technique for breast cancer.

    Keywords: Breast cancer; Pathological image; Computer aided diagnosis; Transfer learning.

  • A Study on Smart Home Voice Control Terminal

    Open Access

    Abstract—With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control.
    This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
    Keywords—Arm microcontroller; Speech recognition; Wireless transceiver; Voice broadcast

  • Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients

    Open Access

    Abstract:  For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which  makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.

    Keywords: Soft Computing; Medical Decision Support; Triage Assessment; Fuzzy Cognitive Maps

  • Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination.

    Open Access

    Abstract:  The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88%  increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone

    Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.

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    A novel neural interfacing electrode array for electrical stimulation and simultaneous recording of EEG/EMG/ENG

    Open Access

    Abstract: Neural interface is man-made information pathway through which biological nerve system could communicate directly with electromechanical devices including computer, robot and even cyborg. This paper introduces a novel neural interfacing electrode array capable of bidirectional information transmission and multi-signals recording. The novel flexible electrode array with 32 channels enables close-looped control and feedback neural interfacing researches with the capability of both electrical stimulation and simultaneous recording of electroencephalogram (EEG), electromyogram (EMG), electroneurogram (ENG) signals. The electrochemical impedance spectroscopy (EIS) measurement of the electrode array was carried out to evaluate the electrode array electrochemical performance in the electrophysiology frequency range. Electrical stimulation to peripheral nerve was performed with various stimulation configuration of ENG electrode site pairs to produce distinct activation patterns. Muscle action potentials of the gastrocnemius of the hind limb indicated different configuration of electrode site pairs could generate distinct stimulating effect. In addition, three groups of in vivo experiments were conducted to demonstrate the recording ability of the electrode array for nerve signals. The data from 32 channels verified the effectiveness of this flexible electrode array in simultaneous recording of EEG/EMG/ENG in vivo.

  • Construction of a focused ultrasound neuromodulation system for the treatment of epileptic seizure

    Open Access

    Abstract: Thus far, Ultrasound has been proved to be useful for noninvasively stimulating brain activity and has hope to play a positive role in the treatment of neurological diseases. Epilepsy is a neurological disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, and sometimes loss of awareness. In this paper, we established a focused ultrasound (FUS) neuromodulation system for the treatment of epilepsy. We used the chemically-induced rat epilepsy model to explore the effect of pulsed focused ultrasound on epilepsy, and obtained preliminary experimental results. We have proved the feasibility of this system through experiments, which can be used in the treatment of epilepsy by ultrasound neuromodulation.