• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.1-9
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• 2012

This paper presents an algorithm for depression diagnosis using the Neural Network with Weighted Fuzzy Membership functions (NEWFM) and heart rate variability (HRV). In the algorithm, 22 different features were initially extracted from the HRV signal by frequency domain, time domain, wavelet transformed, and Poincar$\acute{e}$ transformed feature extraction methods; of these 6 optimal features were selected by significance evaluation using Non-overlap Area Distribution Measurement (NADM) based on NEWFM. The proposed algorithm uses these 6 optimal features to diagnose depression with an accuracy of 95.83%.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.320-324
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• 1996

Digital fetal monitoring system based on the personal computer combined with the digital signal processing board was implemented. The DSP board acquires and digitally processes ultrasound fetal Doppler signal for digital rectification, FIR filtering, autocorrelation function calculation, its peak detection and MEDIAN filtering. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on-line analysis of fetal heart rate change including and variability. I used a recursive technique for autocorrelation function computation method and MEDIAN filter which can greatly reduce the amount of calculation and accuracy. I also implemented analysis algorithm of fetal heart rate change based on normal fetal sample data in order to exact diagnosis.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1159-1165
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• 2011

Telemetry techniques of rats have been used for assessing safety pharmacology of drugs and chemicals. Biological signals including blood pressure and heart rate measured under anesthesia were significantly different from those obtained under normal conditions. The stress of restraint in awake animals can also affect the accuracy of physiological evaluation. This paper details the surgery required to allow key cardiovascular parameters to be determined. The telemetric measurement of cardiovascular parameters such as blood pressure, heart rate, electrocardiograph(ECG) established. We carried out the continuous monitoring of cardiovascular parameters using the telemetry system in F344 rats. During the measurement, no significant changes were observed in the heart rate and blood pressure. ECG signals and body temperature were also constant during the measurement of biological signals. With the results of this study, we conclude that this telemetry system can be applied usefully for the assesment of biological parameters in the rats.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.61-69
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• 2015

This study presents a probabilistic knowledge discovery method to interpret heart rate variability (HRV) based on time and frequency domain indexes, extracted using discrete wavelet transform. The knowledge induction algorithm was composed of two phases: rule generation and rule estimation. Firstly, a rule generation converts numerical attributes to intervals using ROC curve analysis and constructs a reduced ruleset by comparing consistency degree between attribute-value pairs with different decision values. Then, we estimated three measures such as rule support, confidence, and coverage to a probabilistic interpretation for each rule. To show the effectiveness of proposed model, we evaluated the statistical discriminant power of five rules (3 for atrial fibrillation, 1 for normal sinus rhythm, and 1 for both atrial fibrillation and normal sinus rhythm) generated using a data (n=58) collected from 1 channel wireless holter electrocardiogram (ECG), i.e., HeartCall$^{(R)}$, U-Heart Inc. The experimental result showed the performance of approximately 0.93 (93%) in terms of accuracy, sensitivity, specificity, and AUC measures, respectively.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.701-706
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• 2007

The measurement accuracy for heart rate or $SpO_2$ using photoplethysmography (PPG) is influenced by how well the noise from motion artifacts and other sources can be removed. Eliminating the motion artifacts is particularly difficult since its frequency band overlaps that of the basic PPG signal. Therefore, we propose the Periodic Moving Average Filter (PMAF) to remove motion artifacts. The PMAF is based on the quasi-periodicity of the PPG signals. After segmenting the PPG signal on periodic boundaries, we average the $m^{th}$ samples of each period. As a result, we remove the motion artifacts well without the deterioration of the characteristic point.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.9-13
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• 2019

Recently, as the sleep disorder problem of modern people deepens, the interest towards quality of sleep is increasing. To increase the quality of modern people's sleep. This paper has suggested an LED lighting control system according to the sleep stage using PPG sensors of wearable devices. The pulse of the wrist radial artery was measured using a wearable device mounted with PPG sensor, which enables heart rate-measuring, and by using the point that heart rate lowers during stable sleep than non-sleeping, the LED lighting of indoors was controlled, which is the disturbing element when sleeping. For the performance evaluation, a 10-Fold cross analysis was conducted for performance evaluation, and a result of an average accuracy 87.02% was obtained as a result. Therefore, the LED lighting control system according to the sleep stage using a wearable device of this paper is expected to contribute to raise the quality of the user's life.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1370-1378
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• 2018

There is a problem to measure neutral bio-signals during sleep because of inconvenience of attaching lots of sensors. In this study, we measured single electrocardiogram(ECG) signal and analyzed the correlation with sleep. After R-peak detection from ECG signal, we extracted 9 features from time and frequency domain of heart rate variability(HRV). Mean of HRV, RR intervals differing more than 50ms(NN50), and divided by the total number of all RR intervals(pNN50) have significant differences in each sleep stage. Specially, the mean HRV has an average of 87.8% accuracy in classifying sleep and awake status. In the future, the measurement ECG signal minimizes inconvenience of attaching sensors during sleep. Also, it can be substituted for the standard sleep measurement method.

• Journal of Digestive Cancer Research
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• v.12 no.1
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• pp.31-37
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• 2024

Pancreatic cancer is a highly fatal malignancy with a 5-year survival rate of < 10%. Endoscopic ultrasound (EUS) is a useful noninvasive tool for differential diagnosis of pancreatic malignancy and treatment decision-making. However, the performance of EUS is suboptimal, and its accuracy for differentiating pancreatic malignancy has increased interest in the application of artificial intelligence (AI). Recent studies have reported that EUS-based AI models can facilitate early and more accurate diagnosis than other preexisting methods. This article provides a review of the literature on EUS-based AI studies of pancreatic malignancies.

• Korean Journal of Biological Psychiatry
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• v.17 no.2
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• pp.79-85
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• 2010

Objectives : Trauma patients have attentional bias which enforces traumatic memories and causes cognitive errors. Understanding of such selective attention may explain many aspects of the posttraumatic stress disorder(PTSD) symptoms. Methods : We used the rapid serial visual presentation(RSVP) method to verify attentional blink in burn patients with PTSD. International affective picture system(IAPS) was used as stimuli and distracters. In the 'neutral test', patients have been presented series of pictures with human face picture as target stimuli. Each picture had 100ms interval. However the distance between target facial pictures was randomized and recognition of second facial picture accuracy was measured. In the 'stress test', the first target was stress picture which arouses patient emotions instead of the facial picture. Neutral and Stress tests were done with seven PTSD patients and 20 controls. In '85ms test' the interval was reduced to 85ms. The accuracy of recognition of second target facial picture was rated in all three tests. Eighty-five ms study was done with eighteen PTSD patients. Results : Attentional blinks were observed in 100-400ms of RSVP. PTSD patients showed increased recognition rate in the 'stress test' compared with the 'neutral test'. When presentation interval was decreased to 85 ms, PTSD patient showed decrease of attentional blink effect when target facial picture interval was 170ms. Conclusion : We found attentional blink effect could be affected by stress stimulus in burn patients. And attentional blink may be affected by stimulus interval and the character of stimulus. There may be some other specific mechanism related with selective attention in attentional blink especially with facial picture processing.