다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

Neuroelectric and imaging studies of meditation are reviewed. Electroencephalographic measures indicate an overall slowing subsequent to meditation, with theta and alpha activation related to proficiency of practice. Sensory evoked potential assessment of concentrative meditation yields amplitude and latency changes for some components and practices. Cognitive event-related potential evaluation of meditation implies that practice changes attentional allocation. Neuroimaging studies indicate increased regional cerebral blood flow measures during meditation. Taken together, meditation appears to reflect changes in anterior cingulate cortex and dorsolateral prefrontal areas. Neurophysiological meditative state and trait effects are variable but are beginning to demonstrate consistent outcomes for research and clinical applications. Psychological and clinical effects of meditation are summarized, integrated, and discussed with respect to neuroimaging data.

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Meditation States and Traits: EEG, ERP, and Neuroimaging Studies.

Principles and Practice of Sleep Medicine

Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential applications, and investigates the impact of various critical parameters on its performance. BLE represents a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on parameters such as connInterval and connSlaveLatency. According to theoretical results, the lifetime of a BLE device powered by a coin cell battery ranges between 2.0 days and 14.1 years. The number of simultaneous slaves per master ranges between 2 and 5,917. The minimum latency for a master to obtain a sensor reading is 676 μs, although simulation results show that, under high bit error rate, average latency increases by up to three orders of magnitude. The paper provides experimental results that complement the theoretical and simulation findings, and indicates implementation constraints that may reduce BLE performance.

https://www.mdpi.com/1424-8220/12/9/11734/pdf

Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.

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Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance, based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus, this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number for EEMD was also examined.

Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

According to the experimental results and practitioners' subjective experience, we report some hypotheses that may account for meditative phenomena during the practice of Zen-Buddhism. Orthodox Zen-Buddhist practitioners, aiming to prove the most original true-self, discover and uncover the inner energy or light on the way towards their goal. Perception of the inner light can be comprehended as resonance. Uncovering the inner energy optimizes physiological and mental health. In the meditation experiment, a significant correlation was observed between perception of the inner light and electroencephalographic (EEG) alpha blockage. We further examined this phenomenon by recording the EEG from subjects during a blessing that the subjects did not know being given. During the blessing period, significant alpha blocking was observed in experimental subjects who had been practicing meditation for years in preparation for being in resonance with the inner light. This report provides a new insight into the debate that meditation benefits our health.

EEG alpha blocking correlated with perception of inner light during zen meditation.

Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric muscle contraction are clearly represented in the receiving user interface. Muscle fatigue detection is an important application of sEMG. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. A more negative slope value represents a higher muscle fatigue condition. To test the system performance, muscle fatigue detection was examined by having subjects run on a pedaled-multifunctional elliptical trainer for approximately 30 minutes at three loading levels. Ten subjects underwent a total of 60 exercise sessions to provide the experimental data. Results showed that the regression slope gradually decreases as expected, and there is a significant gender difference.

A wireless sEMG recording system and its application to muscle fatigue detection.

Workplace stress-related illness is a serious issue, and consequently many stress reduction methods have been investigated. Aromatherapy is especially for populations that work under high stress. Elementary school teachers are a high-stress working population in Taiwan. In this study, fifty-four elementary school teachers were recruited to evaluate aromatherapy performance on stress reduction. Bergamot essential oil was used for aromatherapy spray for 10 minutes. Blood pressure and autonomic nervous system parameters were recorded 5 minutes before and after the application of the aroma spray. Results showed that there were significant decreases in blood pressure, heart rate, LF power percentage, and LF/HF while there were increases in heart rate variability and HF power percentage ( P .001 ∗ ∗ ∗ ) after application of the aromatherapy spray. Further analysis was investigated by dividing subjects into three background variables (position variables, age variables, gender variables) and anxiety degree groups. All parameters were significantly different for most subgroups, except for the substitute teachers and the light-anxiety group. Parasympathetic nervous system activation was measured after aromatherapy in this study. It encouraged further study for other stress working population by aromatherapy.

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Kang-Ming Chang

Papers

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

EEG alpha blocking correlated with perception of inner light during zen meditation.

A wireless sEMG recording system and its application to muscle fatigue detection.

Aromatherapy Benefits Autonomic Nervous System Regulation for Elementary School Faculty in Taiwan