Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis / 대한의료정보학회지

OBJECTIVES: Heart rate variability (HRV) has gained recognition as a noninvasive marker of autonomic activity. HRV is considered a promising tool in various clinical scenarios. The optimal electrocardiogram (ECG) sampling frequency required to ensure sufficient precision of R–R intervals for HRV analysis has not yet been determined. Here, we aimed to determine the acceptable ECG sampling frequency range by analyzing ECG signals from patients who visited an emergency department with the chief complaint of acute intoxication or overdose. METHODS: The study included 83 adult patients who visited an emergency department with the chief complaint of acute poisoning. The original 1,000-Hz ECG signals were down-sampled to 500-, 250-, 100-, and 50-Hz sampling frequencies with linear interpolation. R–R interval data were analyzed for time-domain, frequency-domain, and nonlinear HRV parameters. Parameters derived from the data on down-sampled frequencies were compared with those derived from the data on 1,000-Hz signals, and Lin's concordance correlation coefficients were calculated. RESULTS: Down-sampling to 500 or 250 Hz resulted in excellent concordance. Signals down-sampled to 100 Hz produced acceptable results for time-domain analysis and Poincaré plots, but not for frequency-domain analysis. Down-sampling to 50 Hz proved to be unacceptable for both time- and frequency-domain analyses. At 50 Hz, the root-mean-squared successive differences and the power of high frequency tended to have high values and random errors. CONCLUSIONS: A 250-Hz sampling frequency would be acceptable for HRV analysis. When frequency-domain analysis is not required, a 100-Hz sampling frequency would also be acceptable.

Distinguishing Quantitative Electroencephalogram Findings between Adjustment Disorder and Major Depressive Disorder

OBJECTIVE: Adjustment disorder (ADJ) is a common diagnosis. However, it is difficult to distinguish ADJ from other major Axis I disorders, such as major depressive disorder (MDD). The aim of this study was to determine the distinguishing neurophysiological characteristics between ADJ and MDD using quantitative analysis of an electroencephalogram (QEEG). METHODS: The study included 30 patients with ADJ and 51 patients with MDD. Resting (eye closed) vigilance controlled EEG recordings were assessed at 19 electrode sites according to the international 10/20 system. QEEG absolute power and coherence were calculated for the delta, theta, alpha and beta bandwidths. RESULTS: Absolute powers of alpha and high beta bands, particularly at the frontocentral area, differed between MDD and ADJ group (p<0.05). Interhemispheric coherence values for the delta and beta bands were lower in the ADJ group than in the MDD group (p<0.05). Intrahemispheric coherence values for the alpha band were also lower in the ADJ group (p<0.05). CONCLUSION: The differences in QEEG power and coherence in our investigation suggest that underlying pathophysiologic mechanisms may be different between ADJ and MDD.

An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection / 대한의료정보학회지

OBJECTIVES: The mucociliary transport system is a major defense mechanism of the respiratory tract. The performance of mucous transportation in the nasal cavity can be represented by a ciliary beating frequency (CBF). This study proposes a novel method to measure CBF by using optical flow. METHODS: To obtain objective estimates of CBF from video images, an automated computer-based image processing technique is developed. This study proposes a new method based on optical flow for image processing and peak detection for signal processing. We compare the measuring accuracy of the method in various combinations of image processing (optical flow versus difference image) and signal processing (fast Fourier transform [FFT] vs. peak detection [PD]). The digital high-speed video method with a manual count of CBF in slow motion video play, is the gold-standard in CBF measurement. We obtained a total of fifty recorded ciliated sinonasal epithelium images to measure CBF from the Department of Otolaryngology. The ciliated sinonasal epithelium images were recorded at 50-100 frames per second using a charge coupled device camera with an inverted microscope at a magnification of x1,000. RESULTS: The mean square errors and variance for each method were 1.24, 0.84 Hz; 11.8, 2.63 Hz; 3.22, 1.46 Hz; and 3.82, 1.53 Hz for optical flow (OF) + PD, OF + FFT, difference image [DI] + PD, and DI + FFT, respectively. Of the four methods, PD using optical flow showed the best performance for measuring the CBF of nasal mucosa. CONCLUSIONS: The proposed method was able to measure CBF more objectively and efficiently than what is currently possible.