Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review / 대한배뇨장애요실금학회지

Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient’s state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction.

Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices / 대한배뇨장애요실금학회지

Due to a mistake in reference by the author submission in this article, an erroneous grant number had been published.

Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices / 대한배뇨장애요실금학회지

Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of “smart health.” Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.

A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation

OBJECTIVES: In an effort to improve hearing aid users’ satisfaction, recent studies on trainable hearing aids have attempted to implement one or two environmental factors into training. However, it would be more beneficial to train the device based on the owner’s personal preferences in a more expanded environmental acoustic conditions. Our study aimed at developing a trainable hearing aid algorithm that can reflect the user’s individual preferences in a more extensive environmental acoustic conditions (ambient sound level, listening situation, and degree of noise suppression) and evaluated the perceptual benefit of the proposed algorithm. METHODS: Ten normal hearing subjects participated in this study. Each subjects trained the algorithm to their personal preference and the trained data was used to record test sounds in three different settings to be utilized to evaluate the perceptual benefit of the proposed algorithm by performing the Comparison Mean Opinion Score test. RESULTS: Statistical analysis revealed that of the 10 subjects, four showed significant differences in amplification constant settings between the noise-only and speech-in-noise situation (P<0.05) and one subject also showed significant difference between the speech-only and speech-in-noise situation (P<0.05). Additionally, every subject preferred different β settings for beamforming in all different input sound levels. CONCLUSION: The positive findings from this study suggested that the proposed algorithm has potential to improve hearing aid users’ personal satisfaction under various ambient situations.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.

Hierarchical Classification of ECG Beat Using Higher Order Statistics and Hermite Model / 대한의료정보학회지

OBJECTIVE: The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithmhave been designed to detect P, QRS, Twave, first. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. Furthermore the conventionalmulticlass classificationmethodmay have skewed results to themajority class, because of unbalanced data distribution. METHODS: The Hermite model of the higher order statistics is good characterization methods for recognizing morphological QRS complex. We applied three morphological feature extraction methods for detecting QRS complex: higher-order statistics, Hermite basis functions andHermitemodel of the higher order statistics.Hierarchical scheme tackle the unbalanced data distribution problem. We also employed a hierarchical classification method using support vector machines. RESULTS:We compared classification methods with feature extraction methods. As a result, our mean values of sensitivity for hierarchical classification method (75.47%, 76.16% and 81.21%) give better performance than the conventionalmulticlass classificationmethod (46.16%). In addition, theHermitemodel of the higher order statistics gave the best results compared to the higher order statistics and the Hermite basis functions in the hierarchical classification method. CONCLUSION: This research suggests that the Hermite model of the higher order statistics is feasible for heartbeat feature extraction. The hierarchical classification is also feasible for heartbeat classification tasks that have the unbalanced data distribution.

The Study on Classification Method of Obsessive-Compulsive Disorder with Schizotypal Trait using Frontal Lobe Function Test / 대한의료정보학회지

OBJECTIVE: The identifying schizotypal trait in obsessive-compulsive disorder (OCD) patients is important to predict clinical course, since those patients are hardly overcome through conventional intervention methods. This paper presents the trial of classification method of obsessive-compulsive disorder with schizotypal trait using Frontal Lobe Function Test (FLFT). METHODS: 110 OCD patients are divided into two groups:27 pure OCD patients, and 83 non-pure OCD patients. After training artificial neural network (ANN) using frontal-lobe function test data of train data (schizophrenia, pure OCD, and normal group), we classify test data (non-pure OCD patients) into one of the three groups. RESULTS: Among the total 83 test data (non-pure OCD patients), 44 patients were classified as schizophrenia, 32 patients as normal, and 7 patients as pure OCD. With respect to the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) data of those classified patients, ordering score in compulsion was significantly different between three groups. Moreover, cluster A socre (Schizoid, Schizotypal) of Personality Diagnostic Questionnaire (PDQ) data showed significant difference between them. CONCLUSION: The results presented that those OCD patients who are classified as schizophrenia using generated model with machine learning technique is tend to have compulsive symptom of arrangement and schizotypal personality disorder.

A Solitary Fibrous Tumor of the Retroperitoneum: CT Findings

The solitary fibrous tumor is a rare spindle cell neoplasm of mesodermal orgin. It usually arises in thepleura, though has recently been reported in the peritoneum, and at nonserosal sites; it is, however, extremelyrare in the retroperitonum. The authors describe one case of a solitary fibrous tumor occurred as a huge complexmass in the retroperitoneum of an adult. Preenhanced CT scans showed that the mass was sharply delineated fromsurrounding organs and that tumor density was the same as that of surrounding musculature. PeripheralCalcifications were noted, and postenhanced scans showed intense, gradual enhancement of solid portions of themass, with nonenhanced cystic portions. Although not specific, solitary fibrous tumors must be included in the differential diagnosis of a large retroperitoneal tumor of complex consistency and with internal calcification.

CT and MR Findings of Primary Hepatic Leiomyosarcoma: A Case Report

We describe the CT and MR findings of primary leiomyosarcoma of the liver and review the radiological findings of the previous reports of this disease. A 35-year-old woman presented with discomfort in the right upper quadrant of the abdomen. On contrast-enhanced CT, a well circumscribed and lobulated, homogeneously hypoattenuating solid mass with slight peripheral enhancement was identified in the caudate lobe of the liver. On spin-echo MR imaging, the tumor showed homogeneous hypointensity on T1-weighted images and hyperintensity on T2-weighted images. Contrast-enhanced T1-weighted imaging showed markedly heterogeneous enhancement throughout the mass.