Abstract: A method of creating an anesthetic consciousness index with an artificial neural network includes, obtaining physiological signals, including electroencephalographic signals and eye movement signals, from subjects during a physiological signal monitoring process; filtering noise out of the physiological signals by empirical mode decomposition (EMD); calculating sample entropy values of the noise-removed physiological signals; obtaining sample entropy value sets of the physiological signals; repeating the aforesaid steps to effectuate measurement, noise-filtering, and sample entropy value calculation of the subjects' physiological signals and thus obtain a sample entropy value set; and applying an artificial neural network in conducting regression analysis of the sample entropy value set and a set of levels of consciousness measured with a physiological signal monitor during the physiological signal monitoring process, thereby creating the anesthetic consciousness index model for evaluating the level of consci

Abstract: A human body balance signals measuring system and the method of analysis thereof that has a measuring device, a filter amplifier, an A/D convertor (analog to digital convertor), a signal receiving module, and a data analyze module. The measuring device is linked with the filter amplifier. The filter amplifier can detect and collect the voltage signals caused by pressure change and then filter and amplify the signals. The signals are send to the A/D convertor to convert the analog circuit signals into digital signals for the receiving module to use these voltage change values for human body center of gravity offset evaluation to obtain the COP (center of pressure) offset and COP offset velocity. The data analyze module uses the measured body center of gravity offset for MSE (multiscale entropy) to quantitative the dynamic of human body center of gravity and verify the accuracy of this measuring system.

Abstract: A psychological stress index measuring system and its analysis method comprise a pulse generator, a transmission-based photo sensor, a signal converter & amplifier, a filter and a processing platform. At first, the transmission-based photo sensor fixed on a user depends on a light transmitting mechanism to detect analog SpO2 (oxyhemoglobin saturation by pulse oximetry) signals. Then, it will be processed and transformed to digital signals and further exported to the processing platform for extraction of PPGA & HBI. Finally, it will pass a median filter, normalization, and development of Psychological Stress Index which is based on the normalized PPGA & HBI as one convenient method to evaluate psychological health.

Abstract: A Fall-risk Evaluation and Balance Stability Enhancement System and method evaluate and treat the sense of body balance. It is focused on measuring the center of pressure (COP) information, evaluating fall risk by the noninvasive physiological signals machines, and improving the body's balance and stability by stimulating physical treatment in feet.

Abstract: A human body balance signals measuring system and the method of analysis thereof that has a measuring device, a filter amplifier, an A/D convertor (analog to digital convertor), a signal receiving module, and a data analyze module. The measuring device is linked with the filter amplifier. The filter amplifier can detect and collect the voltage signals caused by pressure change and then filter and amplify the signals. The signals are send to the A/D convertor to convert the analog circuit signals into digital signals for the receiving module to use these voltage change values for human body center of gravity offset evaluation to obtain the COP (center of pressure) offset and COP offset velocity. The data analyze module uses the measured body center of gravity offset for MSE (multiscale entropy) to quantitative the dynamic of human body center of gravity and verify the accuracy of this measuring system.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e.

Abstract: A system for monitoring patient control analgesia (PCA) is provided. The system is used with or without a web page at bedside or a remote end. With the system, patients obtain good pain caring; doctors are provided with abundant reference data; and vendors get controls on device logistics.

Abstract: An method for analyzing irreversible apneic coma (IAC) for determining the presence of irreversible apneic coma (IAC) by analyzing the heart rate variability of a brain traumatic patient, thereby providing a physician a reference index to determine whether brain death has occurred. This method includes, at first, recording an electrocardiogram (ECG) from a subject. Then, analyzing R-R interval in said electrocardiogram (ECG), and plotting said R-R interval into Poincaré plot, wherein the X coordinate in said Poincaré plot represents R-R interval(n), and n is a 1˜data number. Y coordinate in said Poincaré plot represents RR(n+1). And, finally, quantifying said Poincaré plot, and obtaining semi-major axis (SD1), semi-minor axis (SD2), and SD1/SD2 of said Poincaré plot, as well as Poincaré plot area.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e.

Abstract: An method for analyzing irreversible apneic coma (IAC) for determining the presence of irreversible apneic coma (IAC) by analyzing the heart rate variability of a brain traumatic patient, thereby providing a physician a reference index to determine whether brain death has occurred. This method includes, at first, recording an electrocardiogram (ECG) from a subject. Then, analyzing R-R interval in said electrocardiogram (ECG), and plotting said R-R interval into Poincaré plot, wherein the X coordinate in said Poincaré plot represents R-R interval(n), and n is a 1˜data number. Y coordinate in said Poincaré plot represents RR(n+1). And, finally, quantifying said Poincaré plot, and obtaining semi-major axis (SD1), semi-minor axis (SD2), and SD1/SD2 of said Poincaré plot, as well as Poincaré plot area.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.