Objective We studied the change of psychological behavior and quality of life in children with epilepsy, to explore the treatment strategy to improve their psychological behavior and quality of life. Methods Collected forty children with epilepsy from Hebei General Hospital during March 2015 to October 2015 and 40 normal children in this study. "Epilepsy patients quality of life scale", "Daily life ability scale" and "Sense of uncertainty in hospitalized patients disease scale" test were performed to the participants. The difference of daily psychological behavior ability, sense of uncertainty in illness and quality of life between the two groups were analyzed. Results The epilepsy children’s daily life ability and the quality of life are lower than normal children (P<0.05), the disease uncertainty is higher than normal children (P<0.05). Conclusion The epilepsy children had serious psychological and behavior disorders and lower life quality. Some medical intervention should be taken to alleviate the psychological burden, improve the ability of daily life, reduce the uncertainty in illness, and comprehensively improve the quality of life of children with epilepsy.
Objective To investigate the application of Magnetoencephalograph (MEG), Wada test combined with neuronavigation in the surgical treatment of frontal and temporal epilepsy caused by focal cortical dysplasia (FCD ). Methods The epileptogenic focus and IQ, memory and language examination were performed in 34 patients with frontal and temporal epilepsy caused by FCD. MEG and Wada test were conducted to determine the language and memory advantage hemisphere, and to clarify the scope and memory function of language function areas. Operation was guided by the Medtronic stealhealth 7 surgical navigation system (USA) to remove the FCD and protect nerve function. IQ, memory and language examination were measured 1 year after operation, and the difference was observed before and after operation. The postoperative follow-up was 23 ~ 46 months, curative effect of epilepsy was determined according to the international anti-epilepsy union Engel’s standard. Results Thirty-four patients with epilepsy (21 temporal lobe epilepsy and 13 frontal lobe epilepsy) were included in this study. The examination process of MEG and Wada test was smooth. MEG can accurately locate the position of language function area. Twenty-eight patients’ dominant hemisphere of language was on the left and 6 was on the right side. Wada test can evaluate the patient’s memory function. Twenty-three patients’ dominant hemisphere of memory was located on the left, 8 on the right and 3 on the bilateral hemisphere. Compared with the dominant hemisphere and nondominant hemisphere, the memory score was significantly different (P<0.05). Statistics showed that the verbal IQ and total IQ increased (P<0.05)1 year after operation, but there was no significant change in memory IQ and Performance IQ (P>0.05). FCD patients recovered well without language, memory and limb impairment. The curative effect of epilepsy: 15 cases of Engel’sⅠgrade, 14 cases of Engel’sⅡgrade and 5 cases of Engel’s Ⅲ grade. Conclusion MEG, Wada test combined with neuronavigation was of important value in locating and guiding the surgical resection of FCD in patients with refractory frontal and temporal epilepsy, protecting cortical function, avoiding severe postoperative complications, and improving the therapeutic effect of epilepsy.
ObjectiveTo investigate the clinical value of neuronavigation combined with intraoperative ultrasound in the resection of glioma with epilepsy.MethodsTo review and analyze the clinical data of 47 glioma patients with epilepsy treated by intraoperative ultrasound-assisted neuronavigation during the period from June 30, 2012 to June 30, 2014, and to compare and analyze the extent of gliom resection and the control of epilepsy before and after surgery.ResultsAll the patients had no hematoma, infection or hemiplegia. MRI was reviewed 48 hours after surgery and MRI showed complete resection in 34 cases and subtotal resection in 13 cases. One year after the operation, the seizure control was evaluated. Engel’s class I, 17 cases, Engel’s class II, 20 cases, Engel’s class III, 10 cases. When the nerve function is protected, the tumor is removed and the epileptic seizure is controlled, and the clinical effect is remarkable.ConclusionsNeuronavigation is helpful to locate the lesion and brain functional area and design the surgical approach before surgery, and to guide the location and boundary of the lesion and functional area during surgery. Intraoperative ultrasound has many advantages such as noninvasive, repeatable and real-time examination. Neuronavigation combined with intraoperative ultrasound can achieve maximum resection of gliomas and epileptogenic foci and reduce the incidence of postoperative neurological dysfunction in patients.
ObjectiveTo analyze the effect of mitochondrial ultrastructural changes caused by morphine toxicity on abnormal discharge of cat cerebral cortex, and to explore the possible mechanism of brain function damage caused by morphine dependence.MethodsTwelve domestic cats were divided into control group (3 cats) and morphine exposed group (9 cats) according to the method of random number table. After the model was successfully established by the method of dose increasing, the changes of mitochondrial ultrastructure of cortical neurons were observed under the electron microscope.ResultsElectroencephalogram (EEG) monitoring in morphine exposed group showed that the cortical EEG was widely abnormal, physiological waves were reduced, and abnormal discharges were frequent. And the electron microscopy showed that the number, morphology, internal membrane structure and the inclusion body in the matrix of neurons changed in various aspects. The EEG and electron microscopy of the control group were normal.ConclusionMorphine can damage neurons in the cerebral cortex and lead to abnormal discharge, which is closely related to the ultrastructural changes of neuron mitochondria. The toxicity of morphine mitochondria can be the initial mechanism of energy metabolism dysfunction of brain cells and eventually lead to the disorder of brain electrophysiological function.
Heart sound analysis is significant for early diagnosis of congenital heart disease. A novel method of heart sound classification was proposed in this paper, in which the traditional mel frequency cepstral coefficient (MFCC) method was improved by using the Fisher discriminant half raised-sine function (F-HRSF) and an integrated decision network was used as classifier. It does not rely on segmentation of the cardiac cycle. Firstly, the heart sound signals were framed and windowed. Then, the features of heart sounds were extracted by using improved MFCC, in which the F-HRSF was used to weight sub-band components of MFCC according to the Fisher discriminant ratio of each sub-band component and the raised half sine function. Three classification networks, convolutional neural network (CNN), long and short-term memory network (LSTM), and gated recurrent unit (GRU) were combined as integrated decision network. Finally, the two-category classification results were obtained through the majority voting algorithm. An accuracy of 92.15%, sensitivity of 91.43%, specificity of 92.83%, corrected accuracy of 92.01%, and F score of 92.13% were achieved using the novel signal processing techniques. It shows that the algorithm has great potential in early diagnosis of congenital heart disease.
Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.
The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.
Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.