Patient-Specific Deep Architectural Model for ECG Classification

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition

A high performance surface acoustic wave visible light sensor using novel materials: Bi2S3 nanobelts

Low dimensional Bi2S3 materials are excellent for use in photodetectors with excellent stability and fast response time. In this work, we developed a visible light sensor with good performance based on surface acoustic wave (SAW) devices using Bi2S3 nanobelts as the sensing materials. The SAW delay-line sensor was fabricated on ST-cut quartz with a designed wavelength of 15.8 microns using conventional photolithography techniques. The measured center frequency was 200.02 MHz. The Bi2S3 nanobelts prepared by a facile hydrothermal process were deposited onto SAW sensors by spin-coating. Under irradiation of 625 nm visible light with a power intensity of 170 μW cm−2, the sensor showed a fast and large response with a frequency upshift of 7 kHz within 1 s. The upshift of the frequency of the SAW device is mainly attributed to the mass loading effect caused by the desorption of oxygen from the Bi2S3 nanobelts under visible light radiation

A high performance surface acoustic wave visible light sensor using novel materials: Bi2S3 nanobelts

Low dimensional Bi2S3 materials are excellent for use in photodetectors with excellent stability and fast response time. In this work, we developed a visible light sensor with good performance based on surface acoustic wave (SAW) devices using Bi2S3 nanobelts as the sensing materials. The SAW delay-line sensor was fabricated on ST-cut quartz with a designed wavelength of 15.8 microns using conventional photolithography techniques. The measured center frequency was 200.02 MHz. The Bi2S3 nanobelts prepared by a facile hydrothermal process were deposited onto SAW sensors by spin-coating. Under irradiation of 625 nm visible light with a power intensity of 170 μW cm−2, the sensor showed a fast and large response with a frequency upshift of 7 kHz within 1 s. The upshift of the frequency of the SAW device is mainly attributed to the mass loading effect caused by the desorption of oxygen from the Bi2S3 nanobelts under visible light radiation

Endovascular Embolization of Ruptured Infundibular Dilation of Posterior Communicating Artery: A Case Report

Hemorrhage due to the rupture of the infundibular dilatation of the posterior communicating artery (ID of the PCo-A) occurs infrequently. The preferred treatment of such hemorrhages is surgical clipping through craniotomy. There are few reports about endovascular coil embolization in such cases. We report such a case treated by endovascular embolization. A 35-year-old man, who had experienced 2 episodes of subarachnoid hemorrhage (SAH), was found to have a ruptured ID of the PCo-A by head computed tomography angiography (CTA) and digital subtraction angiography (DSA). We performed stent-assisted endovascular coil embolization through a combined anterior and posterior circulation approach. Postembolization angiography showed absence of contrast filling of the ID of the PCo-A and nonleakage of the contrast agent. The patient recovered well with no complications. SAH recurrence was not recorded during the 1-year followup. The postoperative angiographic result was good. To our knowledge, this is the first case of hemorrhage due to ruptured ID of the PCo-A that was treated by such a technique

Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolution Neural Networks

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1-s electrocardiogram (ECG) segments to time-frequency images, then the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp) and the area under ROC curve (AUC) results are 74.96%, 86.41% and 0.88. When excluding an extreme poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp and AUC values of 79.05%, 89.99% and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode