• Annals of Clinical Neurophysiology
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• v.10 no.1
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• pp.43-47
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• 2008

Background: Acute brachial plexitis is an acute idiopathic inflammatory disease affecting brachial plexus, which is characterized by initial severe pain in shoulder followed by profound weakness of affected arm. This is a retrospective study to evaluate the clinical and electrophysiological profile of acute brachial plexitis. Methods: Sixteen patients with acute brachial plexitis were sampled. The electrodiagnostic studies included motor and sensory nerve conduction studies (NCSs) of the median and ulnar, sensory NCSs of medial and lateral antebrachial cutaneous nerves, and needle electromyography (EMG) of selected muscles of upper extremities and cervical paraspinal muscles. The studies were performed on both sides irrespective of the clinical involvement. Results: In most of our patient, upper trunk was predominantly affected (14 patients, 87.50%). Only two patients showed either predominant lower trunk affection or diffuse affection of brachial plexus. All had an acute pain followed by the development of muscle weakness of shoulder girdle after a variable interval ($7{\pm}8.95$ days). Ten patients (62.50%) had severe disability. In NCSs, the most frequent abnormality was abnormal lateral antebrachial cutaneous sensory nerve action potentials (SNAPs). On needle EMG, all the patients showed abnormal EMG findings in affected muscles. Conclusions: In this study, pain was the presenting feature in all patients, and the territory innervated by upper trunk of the brachial plexus was most frequently involved. The most common NCS abnormality was abnormal SNAP in lateral antebrachial cutaneous nerve. Our findings support that the electrodiagnostic test is useful in localizing the trunk involvement in acute brachial plexitis.

• Journal of The Korean Society of Clinical Toxicology
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• v.11 no.2
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• pp.89-95
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• 2013

Purpose: Glyphosate is widely used and its toxic exposures are not rare. Occasionally, glyphosate intoxication can lead to death. The aim of this study is to analyze clinical findings and fatality in glyphosate intoxication. Methods: Clinical data on acute glyphosate intoxication were prospectively collected at 28 hospitals nationwide between August 2005 and July 2006. The patients' clinical symptoms and characteristics of fatalities were investigated and statistical analysis was performed. Results: Among 105 patients who were finally included, gastrointestinal symptoms(59%) were the most common. A significant difference in the amount ingested was observed between patients with higher systolic blood pressure and those with systolic blood pressure less than or equal to 80 mmHg (p<0.001). The more the patients ingested, the more aggravated their mental status became (p=0.004). Seven patients(6.7%) died, and all of them had ingested greater than or equal to 200 ml. Patients who died had ingested greater amounts than the survivors (p<0.001), and their mental status was worse (p<0.001), and systolic blood pressure was lower (p<0.001). According to the result of logistic regression analysis, relative risk was 24.1-fold higher in the 'poor' mental status group compared with 'good'. Conclusion: Patients who ingested large amounts of glyphosate showed poor mental status and lower blood pressure. Statistical difference in amount ingested, mental status, and systolic blood pressure was observed between survivors and patients who died. Ingested amounts and mental status were the most important factor of the prognosis of glyphosate intoxication.

• The Korean Journal of Pain
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• v.20 no.2
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• pp.174-180
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• 2007

Background: The diagnosis of trigeminal neuralgia (TN) is based on only clinical criteria. The purpose of this study was to estimate the clinical manifestations of TN patients treated at our pain clinic. Methods: A total of 341 patients with TN from Jan. 2004 to Dec. 2006 was evaluated the intensity, site, and onset of pain, facial sensation, duration of pain attack, pain free interval, triggering factors, and effects of the previous treatments with TN specific questionnaire and interview at the first visit of our pain clinic. Results: About 80% of the patients were over 50 years of age and 256 (75%) patients were women. Average durations from first attack of their pain and from current pain attack were 7 years and 16 weeks, respectively. The two most frequently involved trigeminal nerve branches were maxillary (40%) and mandibular (39%) branches. Three quarters of the total patients experienced only paroxysmal pain that lasted less than one minute. About 90% of patients had pain free period at least one time. Most common triggering factors were chewing (88%), brushing teeth (82%), washing face (79%), and talking (70%). Only 16 patients (5%) had no previous treatment and the others had more than one treatment, such as medication (68%) and interventional procedures (35%). The most common reasons for early discontinuation of carbamazepine were dizziness, ataxia, and vomiting. Conclusions: TN has specific clinical features of pain, which should be considered at diagnosis.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.921-930
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• 2022

Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on ¹⁸F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

• 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.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.355-362
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• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.2
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• pp.46-54
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• 2012

We propose a continuously varying contact pressure(CVCP)-adaptive feature extraction algorithm for pulse diagnostic analysis. The CVCP method measures the pulse waveform with continuously increasing contact pressure(CP). This method offer a high resolution signal of the pulse waveform amplitude(PWA) as a function of the contact pressure. Therefore it enables us to overcome the limitation of commercially available pulse-taking devices whose analysis rely on a few number of PWA-CP pairs. We show that an efficient feature extraction algorithm which covers the features of the CVCP-method can be developed by sequentially applying Fast Fourier Transform, peak detection by center-to-edges method, baseline drift removal, detection of the percussion wave upstroke by intersecting tangent method and detection of the analysis region. Finally, by a clinical study with 30 subjects, we show that our CVCP-adaptive feature extraction algorithm detected the upstroke with accuracy of 99.46% and sensitivity of 99.51%, which were about 4.82% and 2.46% increases respectively, compared to a conventional feature extraction method. The proposed CVCP method and the CVCP-adaptive feature extraction algorithm are expected to improve the accuracy in the pulse diagnostic algorithms such as floating/sunken pulse qualities and deficient/excess pulse qualities.