The Determination of the Duration of ElectroconvulsiveTherapy-Induced Seizure Using Local Standard Deviationof the Electroencephalogram Signal and the Changesof the RR Interval of Electrocardiogram

Objectives@#ZZIn electroconvulsive therapy (ECT) research and practice, the precise determination of seizure duration is importantin the evaluation of clinical relevance of the ECT-induced seizure. In this study, we have developed computerized algorithms to assessthe duration of ECT-induced seizure. @*Methods@#ZZSubjects included 5 males and 6 females, with the mean age of 33.1 years. Total 55 ECT sessions were included in theanalysis. We analyzed the standard deviation of a finite block of electroencephalography (EEG) data and the change in the local slope ofRR intervals in electrocardiography (ECG) signals during ECT-induced seizure. And then, we compared the calculated seizure durationsfrom EEG recording (EEG algorithm) and ECG recording (ECG algorithm) with values determined by consensus of clinicians basedon the recorded EEG (EEG consensus), as a gold standard criterion, in order to testify the computational validity of our algorithms. @*Results@#ZZThe mean seizure durations calculated by each method were not significantly different in sessions with abrupt flattenedpostictal suppression and in sessions with non-abrupt flattened postictal suppression. The intraclass correlation coefficients (95% confidenceinterval) of the three methods (EEG algorithm, ECG algorithm, EEG consensus) were significant in the total sessions [0.79(0.70–0.86)], the abrupt flattened postictal suppression sessions [0.84 (0.74–0.91)], and the non-abrupt flattened postictal suppressionsessions [0.67 (0.45–0.84)]. Correlations between three methods were also statistically significant, regardless of abruptness of transition. @*Conclusions@#ZZOur proposed algorithms could reliably measure the duration of ECT-induced seizure, even in sessions with nonabrupttransitions to flat postictal suppression, in which it is typically difficult to determine the seizure duration.

The Determination of the Duration of ElectroconvulsiveTherapy-Induced Seizure Using Local Standard Deviationof the Electroencephalogram Signal and the Changesof the RR Interval of Electrocardiogram

Objectives@#ZZIn electroconvulsive therapy (ECT) research and practice, the precise determination of seizure duration is importantin the evaluation of clinical relevance of the ECT-induced seizure. In this study, we have developed computerized algorithms to assessthe duration of ECT-induced seizure. @*Methods@#ZZSubjects included 5 males and 6 females, with the mean age of 33.1 years. Total 55 ECT sessions were included in theanalysis. We analyzed the standard deviation of a finite block of electroencephalography (EEG) data and the change in the local slope ofRR intervals in electrocardiography (ECG) signals during ECT-induced seizure. And then, we compared the calculated seizure durationsfrom EEG recording (EEG algorithm) and ECG recording (ECG algorithm) with values determined by consensus of clinicians basedon the recorded EEG (EEG consensus), as a gold standard criterion, in order to testify the computational validity of our algorithms. @*Results@#ZZThe mean seizure durations calculated by each method were not significantly different in sessions with abrupt flattenedpostictal suppression and in sessions with non-abrupt flattened postictal suppression. The intraclass correlation coefficients (95% confidenceinterval) of the three methods (EEG algorithm, ECG algorithm, EEG consensus) were significant in the total sessions [0.79(0.70–0.86)], the abrupt flattened postictal suppression sessions [0.84 (0.74–0.91)], and the non-abrupt flattened postictal suppressionsessions [0.67 (0.45–0.84)]. Correlations between three methods were also statistically significant, regardless of abruptness of transition. @*Conclusions@#ZZOur proposed algorithms could reliably measure the duration of ECT-induced seizure, even in sessions with nonabrupttransitions to flat postictal suppression, in which it is typically difficult to determine the seizure duration.

Wearable Physiologic Monitoring System in Health Promotion

With the development of body sensor technology, wearable health monitoring systems have been an emerging information technology in the 'ubiquitous health care' system. Wearable sensors enable long-term continuous physiological monitoring important in health promotion and management of many chronic disorders. In this paper, we present several wearable health monitoring systems developed by different countries and discuss emerging opportunities, as well as, existing challenges such as standardization, administration, validation, and discrepancy issues between technology and clinical implication.

Association of Heart Rate Variability with the Framingham Risk Score in Healthy Adults / 가정의학회지

BACKGROUND: The aim of this study was to investigate the relationship between heart rate variability (HRV), the Framingham risk score (FRS), and the 10-year risk of coronary heart disease (CHD) development among Korean adults. METHODS: The subjects were 85 healthy Korean adults recruited from a health check-up center. The FRS and 10-year risk of CHD development were calculated. RESULTS: The FRS in men was inversely correlated with the standard deviation of all normal to normal RR-intervals (SDNN); the root mean square successive difference (RMSSD); the percentage of successive normal cardiac inter-beat intervals greater than 20 ms, 30 ms, and 50 ms (pNN20, pNN30, pNN50); the low frequency (LF); and the high frequency (HF) (P or = 10%. CONCLUSION: This study therefore indicates that the HRV indices, particularly SDNN, RMSSD, pNN30, LF, and HF may be useful parameters for the assessment of CHD risk. Most notably, the usefulness of these HRV measures as indicators for CHD risk evaluation may be greater among men than among women.

Spatio-Temporal Distribution and Propagation of Temporal Lobe Seizures: Application of Nonlinear Mutual Cross Prediction

BACKGROUND: Nonlinear mutual cross prediction (MCP) characterizes dynamic interdependence among nonlinear systems. MCP also reveal relative strength of the coupling between systems, thus provides information about the direc-tion of interdependence. The aim of this study is to apply MCP algorithm to multi-channel EEG and to characterize spatio- temporal pattern of seizure. METHODS: We analyzed MCP of EEG of three medically intractable temporal lobe epilepsy patients, who underwent temporal lobectomy (left 2, right 1). Asymmetry of nonlinear cross predictability between channels was investigated. Five epochs of interictal EEG free from epileptiform discharge(s) and of ictal EEG were analyzed. RESULTS: In interictal period, both frontal and occipital region appeared a weak driving force while awake and this driving force was further weakened during sleep. Before the onset of the seizure (preictal phase), the intensity of driving system became slightly stronger around seizure foci in 3 out of 8 seizures while no significant change was seen on the naked eyes. However this change was dim and not continuous. At the onset of seizure (ictal phase), 5 out of 8 seizures showed strong driving force around seizure foci. Three seizures without significant change initially had strong driving force as synchronous seizure discharges became built-up and spreading to surrounding areas in the middle of seizure. All seizures showed ipsilateral frontotemporal strong driving force and centroparietal response system, which was typical spatio-temporal distribution of MCP. CONCLUSION: MCP analysis may be a useful method for detecting spatio-temporal distribution and propagation pattern in temporal lobe epilepsy.