Low frequency （≤ 1 Hz） repetitive transcranial magnetic stimulation （rTMS） can affect the excitability of the cerebral cortex and synaptic plasticity. Although this is a common method for clinical treatment of ce...Low frequency （≤ 1 Hz） repetitive transcranial magnetic stimulation （rTMS） can affect the excitability of the cerebral cortex and synaptic plasticity. Although this is a common method for clinical treatment of cerebral infarction, whether it promotes the recovery of motor function remains controversial. Twenty patients with cerebral infarction combined with hemiparalysis were equally and randomly divided into a low frequency rTMS group and a control group. The patients in the low frequency rTMS group were given 1-Hz rTMS to the contralateral primary motor cortex with a stimulus intensity of 90% motor threshold, 30 minutes/day. The patients in the control group were given sham stimulation. After 14 days of treatment, clinical function scores （National Institute of Health Stroke Scale, Barthel Index, and Fugl-Meyer Assessment） improved significantly in the low frequency rTMS group, and the effects were better than that in the control group. We conclude that low frequency （1 Hz） rTMS for 14 days can help improve motor function after cerebral infarction.展开更多
With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and ...With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.展开更多
基金supported by the National Natural Science Foundation of China,No.30540058,30770714the Natural Science Foundation of Beijing of China,No.7052030+2 种基金the Talents Foundation of Organization Department of the Beijing Municipal Committee in Chinathe Beijing Science Plan Project Fund of China,No.Z0005187040191-1the Research Foundation of Capital Medical Development of China,No.2007-2068
文摘Low frequency （≤ 1 Hz） repetitive transcranial magnetic stimulation （rTMS） can affect the excitability of the cerebral cortex and synaptic plasticity. Although this is a common method for clinical treatment of cerebral infarction, whether it promotes the recovery of motor function remains controversial. Twenty patients with cerebral infarction combined with hemiparalysis were equally and randomly divided into a low frequency rTMS group and a control group. The patients in the low frequency rTMS group were given 1-Hz rTMS to the contralateral primary motor cortex with a stimulus intensity of 90% motor threshold, 30 minutes/day. The patients in the control group were given sham stimulation. After 14 days of treatment, clinical function scores （National Institute of Health Stroke Scale, Barthel Index, and Fugl-Meyer Assessment） improved significantly in the low frequency rTMS group, and the effects were better than that in the control group. We conclude that low frequency （1 Hz） rTMS for 14 days can help improve motor function after cerebral infarction.
基金Supported by National Natural Science Foundation of China(Grant No.51405241,51505234,51575283)
文摘With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.
