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A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).

Heartbeat detection is very important for retrieving the vital signs of heart functions. The morphologies and inter-beat intervals of heartbeats can reveal the condition of heart contraction. In this paper, we developed a heartbeat information integration scheme to deal with the information yielded by the energy thresholding and template match methods, which are usually used to detect the heartbeats and match the QRS, respectively. The proposed method are developed in SIMULINK 2.0 and assessed by the MIT/BIH arrhythmia database. The result demonstrated excellent sensitivity of detecting QRS and ventricular premature contraction in the proposed method.

Background EEGs are frequently employed to measure cerebral activations during physical exercise or in response to specific physical tasks. However, few studies have attempted to understand how exercise-state brain activity is modulated by exercise intensity. Methods Ten healthy subjects were recruited for sustained cycle ergometer exercises at low and high resistance, performed on two separate days a week apart. Exercise-state EEG spectral power and phase-locking values (PLV) are analyzed to assess brain activity modulated by exercise intensity. Results The high-resistance exercise produced significant changes in beta-band PLV from early to late pedal stages for electrode pairs F3-Cz, P3-Pz, and P3-P4, and in alpha-band PLV for P3-P4, as well as the significant change rate in alpha-band power for electrodes C3 and P3. On the contrary, the evidence for changes in brain activity during the low-resistance exercise was not found. Conclusion These results show that the cortical activation and cortico-cortical coupling are enhanced to take on more workload, maintaining high-resistance pedaling at the required speed, during the late stage of the exercise period.

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The coma is common in intensive care units. The bedside physical examination provides a means to measuring the neurological status, but it cannot be a continuous evaluation, whereas electroencephalogram (EEG) can reflect the immediate electrical activities of the brain. In this paper, we investigate the spectral parameters, complexity and irregular measures, and spectral entropy in the coma. Compared to the normal subject, the EEG of the coma has a dominance of slow wave, low complexity, less irregularity, and low spectral entropy. This result demonstrates the possibility to use EEG analysis for the monitoring of neurological function.