[show abstract][hide abstract] 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 08/2013; 13(9):751-756. · 1.11 Impact Factor
[show abstract][hide abstract] 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. · 2.41 Impact Factor
[show abstract][hide abstract] 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
[show abstract][hide abstract] 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