[An algorithm based on ECG signal for sleep apnea syndrome detection].

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.

A novel method for automatic identification of motion artifact beats in ECG recordings.

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.

A wireless implantable sensor network system for in vivo monitoring of physiological signals.

A wireless implantable sensor network system (WISNS) is designed for in vivo monitoring physiological signals of a population of animals. WISNS can simultaneously monitor more than 15 animals, communicating three kinds of analog information among sensor nodes. Analog signals are transmitted to relay node at 800-KHz carrier by AM. Relay nodes digitalize and package them into messages, and then forward to the Wireless sensor network by Nordic RF technology (NWSN). Smaller overall dimensions (<2 cm (3)), lower power regulation, and dedicated packaging make the system suitable and compatible for implantable devices. The implantable sensor node, protocol stack of NWSN, and performance of the system are evaluated and optimized with ECG monitoring test of rats. Compared with those commercially available sensor nodes, our implantable one is leading in the weight and volume miniaturization, and our WISNS solution shows huge potential in achieving the compatibility of different animals.

A novel method for detection of the transition between atrial fibrillation and sinus rhythm.

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 distribution 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 normal 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.