Based on the capacitance coupling principle, we studied a capacitive way of non-contact electrocardiogram (ECG) monitoring, making it possible to obtain ECG on the condition that a patient is habilimented. Conductive fabric with a good electrical conductivity was used as electrodes. The electrodes fixed on a bed sheet is presented in this paper. A capacitance comes into being as long as the body gets close to the surface of electrode, sandwiching the cotton cushion, which acts as dielectric. The surface potential generated by heart is coupled to electrodes through the capacitance. After being processed, the signal is suitable for monitoring. The test results show that 93.5% of R wave could be detected for 9 volunteers and ECG with good signal quality could be acquired for 2 burnt patients. Non-contact ECG is harmless to skin, and it has advantages for those patients to whom stickup electrodes are not suitable. On the other hand, it is convenient to use and good for permanent monitoring.
Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.
In order to solve the saturation distortion phenomenon of R component in fingertip video image, this paper proposes an iterative threshold segmentation algorithm, which adaptively generates the region to be detected for the R component, and extracts the human pulse signal by calculating the gray mean value of the region to be detected. The original pulse signal has baseline drift and high frequency noise. Combining with the characteristics of pulse signal, a zero phase digital filter is designed to filter out noise interference. Fingertip video images are collected on different smartphones, and the region to be detected is extracted by the algorithm proposed in this paper. Considering that the fingertip’s pressure will be different during each measurement, this paper makes a comparative analysis of pulse signals extracted under different pressures. In order to verify the accuracy of the algorithm proposed in this paper in heart rate detection, a comparative experiment of heart rate detection was conducted. The results show that the algorithm proposed in this paper can accurately extract human heart rate information and has certain portability, which provides certain theoretical help for further development of physiological monitoring application on smartphone platform.
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
This study proposes an automated neurofibroma detection method for whole-body magnetic resonance imaging (WBMRI) based on radiomics and ensemble learning. A dynamic weighted box fusion mechanism integrating two dimensional (2D) object detection and three dimensional (3D) segmentation is developed, where the fusion weights are dynamically adjusted according to the respective performance of the models in different tasks. The 3D segmentation model leverages spatial structural information to effectively compensate for the limited boundary perception capability of 2D methods. In addition, a radiomics-based false positive reduction strategy is introduced to improve the robustness of the detection system. The proposed method is evaluated on 158 clinical WBMRI cases with a total of 1,380 annotated tumor samples, using five-fold cross-validation. Experimental results show that, compared with the best-performing single model, the proposed approach achieves notable improvements in average precision, sensitivity, and overall performance metrics, while reducing the average number of false positives by 17.68. These findings demonstrate that the proposed method achieves high detection accuracy with enhanced false positive suppression and strong generalization potential.
Bioartificial liver support system (BALSS) provides a new way to treat liver failure and leaves more time for patients who are waiting for liver transplantation. It has detoxification function as well as the human liver, at the same time it can provide nutrition and improve the internal environment inside human body. Bioreactors and hepatocytes with good biological activity are the cores of BALSS which determine the treatment effect. However, in the course of prolonged treatment, the function and activity of hepatocytes might be greatly changed which could influence the efficacy. Therefore, it is very important to detect the status of the hepatocytes in BALSS. This paper presents some common indicators of cell activity, detoxification and synthetic functions, and also introduces the commonly detection methods corresponding to each indicator. Finally, we summarize the application of detection methods of the hepatocyte status in BALSS and discuss its development trend.
Objective To explore the clinical value of Persyst automatic detection of spike waves in adult patients with temporal lobe epilepsy. Methods EEG recordings were continuously concluded from the Epilepsy Unit of the First Affiliated Hospital of Soochow University during 2019.1.1 to 2019.12.31. Two EEG experts certified by the Chinese Anti-Epileptic Association marked interictal epileptic discharge in the long-time electroencephalogram that meet the criteria. Consistent results of the two experts were seen as the "golden standard". The sensitivity and false positive rates were calculated compared with the automatic test results of Persyst version 11, 13 and 14. Results 7 cases were included, each with a recording time of 24~25 hours and a total of 169 hours. Two expert readers achieved the consistency of 43.09%. Spike waves detected automatically were much more than manually. The sensitivity was as high as 62.26%, 77.0% and 67.28%. The lowest false positive rate was 0.37/min, 0.85/min and 0.46/min respectively. Automatic analysis achieved an average workload reduction of 14.59%~37.05%. Conclusions Persyst automatic spike detection has the acceptable sensitivity and false positive rate. It differs from versions and need to be further combined with expert readers.Less workload and accuracy can be balanced by setting reasonable perception parameter.
ObjectiveTo investigate the effectiveness of probe-based near infrared autofluorescence (AF) technology in the identification and functional protection of parathyroid gland (PG) during endoscopic total thyroidectomy. MethodsWe retrospectively collected the clinical data of 160 patients who underwent total thyroidectomy with bilateral central compartment lymph node dissection due to papillary thyroid carcinoma in Chongqing General Hospital from 1 July 2023 to 31 January 2024. Among them, 80 patients who used probe-based near infrared AF technology to identify the PGs were categorized as the AF group, 80 patients who used naked eye (NE) to identify the PGs were categorized as the NE group. The number of PGs identified, inadvertently removed, preserved in situ and autotransplanted, the incidence of postoperative hypoparathyroidism, and operative time were compared between the two groups. ResultsThe incidence of transient hypoparathyroidism was significantly lower in the AF group than that of the NE group [21.25% (17/80) vs. 43.75% (35/80), χ2=9.231, P=0.002], with no cases of permanent hypoparathyroidism in either group. The AF group had significantly more PGs identified and preserved in situ than the NE group (P<0.05) , but had significantly fewer PGs inadvertently removed and autotransplanted than the NE group (P<0.05). The AF group identified the first PG earlier than the NE group (4 min vs. 5 min, P<0.001). But there was no statistically difference in the operative time between the two groups (90 min vs. 94 min, P=0.052). ConclusionThe probe-based near infrared AF technology can help surgeons better identify and protect PGs during surgery, reducing the incidence of postoperative transient hypoparathyroidism.
Epilepsy is a complex and widespread neurological disorder that has become a global public health issue. In recent years, significant progress has been made in the use of wearable devices for seizure monitoring, prediction, and treatment. This paper reviewed the applications of invasive and non-invasive wearable devices in seizure monitoring, such as subcutaneous EEG, ear-EEG, and multimodal sensors, highlighting their advantages in improving the accuracy of seizure recording. It also discussed the latest advances in the prediction and treatment of seizure using wearable devices.
As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.