Abstract: A facial stroking detection method includes a detecting step and a determining step. The detecting step includes a pre-processing step, a feature extracting step and a feature selecting step. In the pre-processing step, an image is captured by an image capturing device, and the image is pre-processed so as to obtain a post-processing image. In the feature extracting step, a plurality of image features are extracted from the post-processing image so as to form an image feature set. In the feature selecting step, a determining feature set is formed by selecting a part of the image features from the image feature set and entered into a classifier. In the determining step, wherein the classifier provides a determining result according to the determining feature set.

Abstract: A heart rate detection method includes a facial image data acquiring step, a feature points recognizing step, an effective displacement signal generating step and a heart rate determining step. The feature points recognizing step is for recognizing a plurality of feature points, wherein a number range of the feature points is from three to twenty, and the feature points include a center point between two medial canthi, a point of a pronasale and a point of a subnasale of the face. The effective displacement signal generating step is for calculating an original displacement signal, wherein the original displacement signal is converted to an effective displacement signal. The heart rate determining step is for transforming the effective displacement signals of each of the feature points to an effective spectrum, wherein a heart rate is determined from one of the effective spectrums corresponding to the feature points, respectively.

Abstract: A heart rate detection method includes a facial image data acquiring step, a feature points recognizing step, an effective displacement signal generating step and a heart rate determining step. The feature points recognizing step is for recognizing a plurality of feature points, wherein a number range of the feature points is from three to twenty, and the feature points include a center point between two medial canthi, a point of a pronasale and a point of a subnasale of the face. The effective displacement signal generating step is for calculating an original displacement signal, wherein the original displacement signal is converted to an effective displacement signal. The heart rate determining step is for transforming the effective displacement signals of each of the feature points to an effective spectrum, wherein a heart rate is determined from one of the effective spectrums corresponding to the feature points, respectively.

Abstract: A facial stroking detection method includes a detecting step and a determining step. The detecting step includes a pre-processing step, a feature extracting step and a feature selecting step. In the pre-processing step, an image is captured by an image capturing device, and the image is pre-processed so as to obtain a post-processing image. In the feature extracting step, a plurality of image features are extracted from the post-processing image so as to form an image feature set. In the feature selecting step, a determining feature set is formed by selecting a part of the image features from the image feature set and entered into a classifier. In the determining step, wherein the classifier provides a determining result according to the determining feature set.

Abstract: This invention discloses a system and a method for extracting VF signal in ECG recorded during uninterrupted CPR. The present invention provides a method for extracting a Ventricular fibrillation (VF) signal in Electrocardiography (ECG), comprising: receiving an ECG signal; adding a plurality of shadowing functions to the ECG signal, to obtain a plurality of modification signals; decomposing the plurality of modification signals by using an Empirical Mode Decomposition (EMD) method, to generate a plurality of Intrinsic Mode Functions (IMFs); calculating the sum of IMFs in different frequency regions based on time sequence, dividing by a number of the shadowing signal, to obtain a plurality of modification intrinsic mode functions; combining the plurality of modification IMFs with the same property, to obtain a shape function; modeling the shape functions to obtain a compression signal; and subtracting the compression signal from the ECG signal based on time sequence, to obtain the VF signal.

Abstract: This invention discloses a system and a method for extracting VF signal in ECG recorded during uninterrupted CPR. The present invention provides a method for extracting a Ventricular fibrillation (VF) signal in Electrocardiography (ECG), comprising: receiving an ECG signal; adding a plurality of shadowing functions to the ECG signal, to obtain a plurality of modification signals; decomposing the plurality of modification signals by using an Empirical Mode Decomposition (EMD) method, to generate a plurality of Intrinsic Mode Functions (IMFs); calculating the sum of IMFs in different frequency regions based on time sequence, dividing by a number of the shadowing signal, to obtain a plurality of modification intrinsic mode functions; combining the plurality of modification IMFs with the same property, to obtain a shape function; modeling the shape functions to obtain a compression signal; and subtracting the compression signal from the ECG signal based on time sequence, to obtain the VF signal.

Abstract: This invention discloses a system and method for extracting VF signal in ECG recorded during uninterrupted CPR. The method and system applies an adaptive algorithm incorporating the EMD and least mean square (LMS) filtering to effectively model the CPR artifacts such as chest compression signals. Thus, A VF signal in ECG recorded during uninterrupted CPR can be extracted without deteriorating the reliability of the waveform parameter (i.e. AMSA) of shockability. The present invention enables uninterrupted CPR performed during recording ECG for accessing the shockability, so that an increase the probability of successful resuscitation is achieved.

Abstract: This invention discloses a system and method for extracting VF signal in ECG recorded during uninterrupted CPR. The method and system applies an adaptive algorithm incorporating the EMD and least mean square (LMS) filtering to effectively model the CPR artifacts such as chest compression signals. Thus, A VF signal in ECG recorded during uninterrupted CPR can be extracted without deteriorating the reliability of the waveform parameter (i.e. AMSA) of shockability. The present invention enables uninterrupted CPR performed during recording ECG for accessing the shockability, so that an increase the probability of successful resuscitation is achieved.

Abstract: A computer-assisted method for quantitative characterization and treatment of ventricular fibrillation includes acquiring a time series of a ventricular fibrillation (VF) signal using a probe from a patient experiencing VF, subtracting the mean from the time series of the VF signal, calculating a cumulative VF signal after the mean is subtracted from the time series of the VF signal, segmenting the cumulative VF signal by a plurality of sampling boxes, calculating the root-mean-square of the cumulative VF signal as a function of the sampling box size , extracting an exponent of the root-mean-square of the cumulative VF signal as a function of the sampling box size, applying electrical defibrillation to the patient if the exponent is below a predetermined value, and applying cardiopulmonary resuscitation (CPR) to the patient if the exponent is above a predetermined value.

Abstract: A computer-assisted method for quantitative characterization and treatment of ventricular fibrillation includes acquiring a time series of a ventricular fibrillation (VF) signal using a probe from a patient experiencing VF, subtracting the mean from the time series of the VF signal, calculating a cumulative VF signal after the mean is subtracted from the time series of the VF signal, segmenting the cumulative VF signal by a plurality of sampling boxes, calculating the root-mean-square of the cumulative VF signal as a function of the sampling box size , extracting an exponent of the root-mean-square of the cumulative VF signal as a function of the sampling box size, applying electrical defibrillation to the patient if the exponent is below a predetermined value, and applying cardiopulmonary resuscitation (CPR) to the patient if the exponent is above a predetermined value.