Abstract: An LED driving system includes a converting circuit, configured to convert a first voltage to a second voltage, having a voltage difference from the first voltage based on a control input; a driving circuit coupled to the converting circuit to receive the second voltage and configured to generate an output signal based on the second voltage; and a controlling circuit coupled to the driving circuit and configured to control a luminance of an LED based on the output signal.

Abstract: A method and an apparatus for analyzing heart rate Variability (HRV), and use thereof are provided. A low-cost, portable and wearable signal acquisition device is utilized to acquire electrocardiography (ECG) signals of epilepsy patients for 24 hours before treatment, and a time domain index, a frequency domain index and a nonlinear index of the ECG during a long term and during a short term are calculated with a programmed HRV analysis method, and the efficacy of vagus nerve stimulation (VNS) treatment for patients with medically intractable epilepsy is accurately and efficiently predicted based on characteristic parameters for characterizing an effect level of the vagus nerve regulating the heart rate, i.e., vagus nerve activity, thereby avoiding unnecessary costs and avoiding the delay of the optimal treatment timing. In addition, the characteristic parameters obtained by the HRV analysis on the ECG may be utilized to clearly select VNS treatment indication patients.

Abstract: A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

Abstract: A method for analyzing heart rate variability, and an apparatus and use thereof, the method for analyzing heart rate variability including collecting ECG data in vitro; digitizing and denoising the ECG data; forming the processed ECG data into a sinus NN interval sequence; selecting sinus NN interval data of 4 hours in an awake state; performing MSE calculation on the sinus NN interval sequence of 4 hours in an awake state; and extracting parameters representing the complexity of a heart rate by using MSE curves. The present invention may provide accurate and efficient screening for drug-refractory epilepsy patients who are suitable for vagus nerve stimulation surgery, thus avoiding unnecessary expenses, and avoiding missing the most opportune moment for treatment. At the same time, patients suitable for VNS surgery are selected by using MSE complexity feature parameters of ECG, thus improving the overall efficacy of VNS therapy.

Abstract: A method and an apparatus for analyzing heart rate Variability (HRV), and use thereof are provided. A low-cost, portable and wearable signal acquisition device is utilized to acquire electrocardiography (ECG) signals of epilepsy patients for 24 hours before treatment, and a time domain index, a frequency domain index and a nonlinear index of the ECG during a long term and during a short term are calculated with a programmed HRV analysis method, and the efficacy of vagus nerve stimulation (VNS) treatment for patients with medically intractable epilepsy is accurately and efficiently predicted based on characteristic parameters for characterizing an effect level of the vagus nerve regulating the heart rate, i.e., vagus nerve activity, thereby avoiding unnecessary costs and avoiding the delay of the optimal treatment timing. In addition, the characteristic parameters obtained by the HRV analysis on the ECG may be utilized to clearly select VNS treatment indication patients.

Abstract: A method and an apparatus for analyzing heart rate Variability (HRV), and use thereof are provided. A low-cost, portable and wearable signal acquisition device is utilized to acquire electrocardiography (ECG) signals of epilepsy patients for 24 hours before treatment, and a time domain index, a frequency domain index and a nonlinear index of the ECG during a long term and during a short term are calculated with a programmed HRV analysis method, and the efficacy of vagus nerve stimulation (VNS) treatment for patients with medically intractable epilepsy is accurately and efficiently predicted based on characteristic parameters for characterizing an effect level of the vagus nerve regulating the heart rate, i.e., vagus nerve activity, thereby avoiding unnecessary costs and avoiding the delay of the optimal treatment timing. In addition, the characteristic parameters obtained by the HRV analysis on the ECG may be utilized to clearly select VNS treatment indication patients.

Abstract: A method for analyzing heart rate variability, and an apparatus and use thereof, the method for analyzing heart rate variability including collecting ECG data in vitro; digitizing and denoising the ECG data; forming the processed ECG data into a sinus NN interval sequence; selecting sinus NN interval data of 4 hours in an awake state; performing MSE calculation on the sinus NN interval sequence of 4 hours in an awake state; and extracting parameters representing the complexity of a heart rate by using MSE curves. The present invention may provide accurate and efficient screening for drug-refractory epilepsy patients who are suitable for vagus nerve stimulation surgery, thus avoiding unnecessary expenses, and avoiding missing the most opportune moment for treatment. At the same time, patients suitable for VNS surgery are selected by using MSE complexity feature parameters of ECG, thus improving the overall efficacy of VNS therapy.

Abstract: A method and an apparatus for analyzing heart rate Variability (HRV), and use thereof are provided. A low-cost, portable and wearable signal acquisition device is utilized to acquire electrocardiography (ECG) signals of epilepsy patients for 24 hours before treatment, and a time domain index, a frequency domain index and a nonlinear index of the ECG during a long term and during a short term are calculated with a programmed HRV analysis method, and the efficacy of vagus nerve stimulation (VNS) treatment for patients with medically intractable epilepsy is accurately and efficiently predicted based on characteristic parameters for characterizing an effect level of the vagus nerve regulating the heart rate, i.e., vagus nerve activity, thereby avoiding unnecessary costs and avoiding the delay of the optimal treatment timing. In addition, the characteristic parameters obtained by the HRV analysis on the ECG may be utilized to clearly select VNS treatment indication patients.

Abstract: A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.