In Wireless Sensor Network (WSN), cluster-based topology is believed to be an effective way for balancing energy consumption and prolonging the network lifespan. However, the clustering process itself can be an energy cost behavior, especially when it is executed periodically. Moreover, little attention has been paid to combine sleeping scheduling with topology formation. In order to solve the above problem, a novel distributed clustering algorithm called Adaptive Energy Efficient Clustering (AEEC) is proposed to maximize network lifetime in this study. Optimizations including the restricted global re-clustering, intra-cluster node sleeping scheduling and adaptive transmission range adjustment are introduced to fulfill the task of energy conservation, while connectivity and coverage is guaranteed. Simulation demonstrates that a great amount of energy is saved for sensed data transmission rather than control packet broadcast, and thus the network lifetime is extended significantly
In the research of ECG signal identity recognition, most of them adopt the method of feature extraction and recognition model separation, extract the time domain features, transform domain features of the original signal, or combine the features with the cross domain. Then the model is used to complete the recognition and classification. In this paper, an advanced improved convolutional neural network model is proposed, which integrates feature extraction and classification to complete identity recognition. ECG data selected from the ecg-id database and MIT-BIH arrhythmia database are directly sent to the model for automatic sign extraction after hierarchical denoising with wavelet tools and then identified. This method achieves the highest recognition rate of 98.49% on ecg-id database and 99.35% on ECG data of MIT-BIH arrhythmia database. The high reliability of the algorithm and the universality of wireless sensors in mobile devices make this research has high commercial value.
Atrial Fibrillation is a common arrhythmia. RR interval irregularity and P wave disappeared and replaced by continuous f-wave is the two important ECG manifestations of atrial fibrillation. RR interval irregularity can also be reflected in other types of arrhythmia, P Wave or f wave is a weak signal, its feature point detection is difficult and shape features are difficult to grasp. Therefore, this paper proposes a method combining manual extraction features and neural network extraction features for atrial fibrillation detection. Experiments were performed using the MIT-BIH atrial fibrillation database. The ECG signals were first processed into equal-length data, and then the 180 dimensional time-frequency domain features were manually extracted and combined with the improved 128 dimensional features extracted by the neural network. The extracted feature input integration model, the sub-model includes Decision Tree, Random forest, GBDT, XGBoost, LightGBM.In the final experiment, the stacking model was used. The accuracy, accuracy, recall rate, F1 and AUC were 99.1%, 98.9%, 98.9%, 98.9%, and 99.4%. This method is better than the single model, which provided a very good measure for detecting atrial fibrillation.
Sleep apnea syndrome is a sleep disease that may lead to sudden death. Long term apnea syndrome can cause chronic cerebral hypoxia, hypertension, cardiovascular and cerebrovascular complications. At present, PSG is the most reliable method for diagnosis. But the diagnosis of PSG is complex and expensive. Electrocardiograph(ECG) and portable medical equipment have been widely used nowadays, which makes the acquisition of ECG signal more and more popular and convenient. In this paper, a convolution neural network based on ECG signal is proposed to predict apnea syndrome, the accuracy and sensitivity of this CNN model for apnea syndrome classification are 94% and 88% respectively. The results show that this method has the advantages of low cost and low complexity.
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