Sung Pil Cho

Detection of Arousals in Patients with Respiratory Sleep Disorders Using a Single Channel EEG

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support vector machine (SVM) classifier using a single channel sleep electroencephalogram (EEG). The performance of our method has been assessed using polysomnographic (PSG) recordings of nine patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). By the proposed method, we could obtain sensitivity of 87.92% and specificity of 95.56% for the training sets, and sensitivity of 75.26% and specificity of 93.08% for the testing sets, respectively. We have shown that proposed method was effective for detecting the arousal events

Adaptive Noise Canceling of Electrocardiogram Artifacts in Single Channel Electroencephalogram

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

Minimization of artifact using adaptive digital filter during the oscillometric blood pressure measurement

Artifacts caused during the oscillometric blood pressure measurement can degrade the quality of data measured. In this paper, the stepped deflation artifact(SDA) and motion artifact have been dealt with. The former and the latter are due to the bleed valve switching and cuff movements, respectively. The adaptive impulse correlated filter (AICF) was applied to remove the stepped deflation artifact. This AICF is performed using a parametric method which use Finite Impulse Response(FIR) structure. A capacitive sensor was utilized in order to minimize the motion artifact caused by cuff movements. A control signal formed from the capacitance data with respect to the motion artifact was applied to the adaptive digital filter. The removal of artifact was worked almost perfectly without reducing the information in the blood pressure pulsation signal.

The Novel Method for the Fetal Electrocardiogram Extraction from the Abdominal Signal

In this paper, we have proposed a new method to extract the fetal ECG from a pregnant womans abdominal signal using least square acceleration (LSA) filter and adaptive impulse correlation (AIC) filter. To evaluate the performance, the proposed method and other fetal ECG extraction techniques were processed using the synthetic and real ECG data and then the results were compared. According to comparative results, the proposed method is powerful and successful for extracting the fetal ECG. It was able to separate perfectly even though the fetal beats overlap with the QRS wave of the maternal beats and to extract fetal ECG using any single-channel abdominal signal measured from pregnant womans abdominal surface. Also, it could be implemented easily by fast computation time and simple structure. It is sure that our method could be useful for portable fetal monitoring system.

Detection of EEG Arousals in Patients with Respiratory Sleep Disorder

This paper describes the detection of arousals from sleep in patients with respiratory sleep disorder using a single channel EEG signal and support vector machine classifier. Determining the occurrence and the frequency of occurrence of arousals from sleep is very important because it is directly related to the quality of sleep. In this paper we used twenty polysomnographic recordings of patients with respiratory sleep disorder. Six recordings were used as training sets and fourteen recordings were used as test sets. We extracted three types of features, which are six indices relating to sleep states, the powers of each of four frequency bands and variations of power of EEG frequency, using time-frequency analysis. We detected arousals from sleep using the above features and SVM classifier. From the results, the sensitivity of 79.65% and the specificity of 89.52% were obtained. The error between the total arousal time detected by the proposed method and the annotated data was 15.09±10.76 min and it showed the possibility of application for the detection of arousal from sleep using a single channel EEG signal.