Chao Huang

Zhejiang University, Hangzhou, Zhejiang Sheng, China

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Publications (7)6.69 Total impact

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    ABSTRACT: The diagnosis of sleep apnea syndrome (SAS) has a significant importance in clinic for preventing diseases of hypertention, coronary heart disease, arrhythmia and cerebrovascular disorder, etc. This study presents a novel method for SAS detection based on single-channel electrocardiogram (ECG) signal. The method preprocessed ECG and detected QRS waves to get RR signal and ECG-derived respiratory (EDR) signal. Then 40 time- and spectral-domain features were extracted to normalize the signals. After that support vector machine (SVM) was used to classify the signals as "apnea" or "normal". Finally, the performance of the method was evaluated by the MIT-BIH Apnea-ECG database, and an accuracy of 95% in train sets and an accuracy of 88% in test sets were achieved.
    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 10/2013; 30(5):999-1002.
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    ABSTRACT: Vanishing point is an important concept in the sensors' self-calibration method. For traditional self-calibration method based on vanishing point, it's computationally intensive, real-time not high and noise sensitive. A novel method of sensor self-calibration based on rectangular vanishing point characteristics is proposed to solve all these problems mentioned above. Firstly, four template images are captured from different angles and locations. Subsequently, the vanishing points are calculated by using the coordinates of the four vertices in rectangle. Finally, the novel sensor parameter equation is proposed by using the geometry properties of rectangle and the vanishing points. Some numerical simulations are made to test the validity and robustness of the proposed algorithm.
    Proc SPIE 08/2013;
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    ABSTRACT: This paper presents a novel method for automatic identification of motion artifact beats in ECG recordings. The proposed method is based on the ECG complexes clustering, fuzzy logic and multi-parameters decision. Firstly, eight simulated datasets with different signal-to-noise ratio (SNR) were built for identification experiments. Results show that the identification sensitivity of our method is sensitive to SNR levels and acts like a low-pass filter that matches the cardiologists' recognition, while the Norm FP rate and PVB FP rate keep significantly low regardless of SNR. Furthermore, a simulated dataset including random durations of motion activities superimposed segments and two clinical datasets acquired from two different commercial recorders were adopted for the evaluation of accuracy and robustness. The overall identification results on these datasets were: sensitivity >94.69%, Norm FP rate <0.60% and PVB FP rate <2.65%. All the results were obtained without any manual threshold adjustment according to the priori information, thus dissolving the drawbacks of previous published methods. Additionally, the total cost time of our method applied to 24 h recordings is less than 1 s, which is extremely suitable in the situation of magnanimity data in long-term ECG recordings.
    Annals of Biomedical Engineering 03/2012; 40(9):1917-28. · 3.23 Impact Factor
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    ABSTRACT: A multi-parameter monitoring system mainly includes invasive blood pressure, PPG and electrocardiogram was designed. We analyzed these signals and extracted some parameters from them, such as SpO2, PPGA, RC, AR, Ps, Pd, Pm, IBPA, IBPI, R and C, which are provided as bases for judgment of doctors. A validation clinical experiment was analyzed, and the results confirmed that the system realized the monitoring of cardiovascular parameters during anesthesia.
    Biomedical Engineering and Biotechnology (iCBEB), 2012 International Conference on; 01/2012
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    ABSTRACT: Atrial fibrillation (AF) has been considered as a growing epidemiological problem in the world, with a substantial impact on morbidity and mortality. Ambulatory electrocardiography (e.g., Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided. This study presents a combined method to achieve high accuracy in AF detection. Firstly, we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve, which were then classified by a combination analysis of P wave and RR interval. The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity (98.2% and 97.5%, respectively) were achieved. Further, we developed a dataset of 24-h paroxysmal AF Holter recordings (n=45) to evaluate the performance in clinical practice, which yielded satisfactory accuracy (sensitivity=96.3%, specificity=96.8%).
    Journal of Zhejiang University SCIENCE B 01/2012; 13(9):751-756. · 1.11 Impact Factor
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    ABSTRACT: Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distri bution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH nor mal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.
    IEEE Transactions on Biomedical Engineering 05/2011; · 2.35 Impact Factor
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    ABSTRACT: Photoplethysmogram (PPG) of pulse wave has been proposed for analgesia monitoring recently with most attentions paid to its magnitude and little attention to its morphology. Therefore, effect of nociceptive stimuli on the morphology of PPG was studied using a morphological parameter named area ratio (AR). Fifty patients, ASA I or II, scheduled for laparoscopy surgery under general anaesthesia were enrolled. They were anaesthetized using propofol and remifentanil, and their PPG signals were recorded. Tracheal intubation was used as a nociceptive stimulus. Off-line analysis showed that the morphology of PPG was influenced by the intubation. The AR increased during intubation and returned to the initial level. Its distributions before intubation (0.687±0.153) and during intubation (0.862± 0.125) were very highly significantly different (P
    01/2011;