Epilepsy is a common chronic neurological disorder of the brain. Clinically, epileptic seizures are usually detected via the continuous monitoring of electroencephalogram (EEG) signals by experienced neurophysiologists.In order to detect epileptic seizures automatically with a satisfactory precision, a new method is proposed which defines hybrid features that could characterize the epileptiform waves and classify single-channel EEG signals.The hybrid features consist of both the ones usually used in EEG signal analysis and the Kraskov entropy based on Hilbert-Huang Transform which is proposed for the first time. With the hybrid features, EEG signals are classified and the epileptic seizures are detected.Three datasets are used for test on three binary-classification problems defined by clinical requirements for epileptic seizures detection. Experimental results show that the accuracy, sensitivity and specificity of the proposed methods outperform two state-of-the-art methods, especially on the databases containing signals from different sources.The proposed method provides a new avenue to assist neurophysiologists in diagnosing epileptic seizures automatically and accurately.
Radio Frequency Identification (RFID) is a promising technology for automated non-line-of-sight object identification. Traditional research of RFID middleware does not concern the business definition of RFID event and related integration with enterprise application system. Considering the requirement of logistics tracking and tracing service based on RFID technology, RFID semantic events which based on XML elements are used to describe the status and information of product logistics. In order to consistent with different business types, the classification and element definition of the RFID events are designed. Through using buffered priority queue, RFID complex events are processed simultaneously and scheduled in real time. An integration framework of RFID events with ERP system is put forward. Different business documents from ERP system can be parsed and generated into uniform XML files and then converted into objects. As a result, RFID complex event is used to control business workflow and provides the warning signal of operation errors. Finally, the RFID-based traceability of product is realized through different query methods.
Abstract Automatic and continuous blood pressure monitoring is important for preventing cardiovascular diseases such as hypertension. The evaluation of medication effects and the diagnosis of clinical hypertension can both benefit from continuous monitoring. The current generation of wearable blood pressure monitors frequently encounters limitations with inadequate portability, electrical safety, limited accuracy, and precise position alignment. Here, we present an optical fiber sensor-assisted smartwatch for precise continuous blood pressure monitoring. A fiber adapter and a liquid capsule were used in the building of the blood pressure smartwatch based on an optical fiber sensor. The fiber adapter was used to detect the pulse wave signals, and the liquid capsule was used to expand the sensing area as well as the conformability to the body. The sensor holds a sensitivity of -213µw/kPa, a response time of 5 ms, and high reproducibility with 70000 cycles. With the assistance of pulse wave signal feature extraction and a machine learning algorithm, the smartwatch can continuously and precisely monitor blood pressure. A wearable smartwatch featuring a signal processing chip, a Bluetooth transmission module, and a specially designed cellphone APP was also created for active health management. The performance in comparison with commercial sphygmomanometer reference measurements shows that the systolic pressure and diastolic pressure errors are -0.35 ± 4.68 mmHg and 2.54 ± 4.07 mmHg, respectively. These values are within the acceptable ranges for Grade A according to the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The smartwatch assisted with an optical fiber is expected to offer a practical paradigm in digital health.
This paper introduced the structure principle and the signal processing technique of the omnidirectional gasflow style horizontal posture sensor. The heat sensitive resistance was used as heat source and sensitive unit, and the natural convection of the gas was produced in a hermetic chamber. The posture signal of carrier was got and processed by hardware circuit and software compensation technique, so it can measure the omnidirectional horizontal posture of the carrier when the carrier were in any yawing direction.
Audio-visual speech recognition (AVSR) research has gained a great success recently by improving the noise-robustness of audio-only automatic speech recognition (ASR) with noise-invariant visual information. However, most existing AVSR approaches simply fuse the audio and visual features by concatenation, without explicit interactions to capture the deep correlations between them, which results in sub-optimal multimodal representations for downstream speech recognition task. In this paper, we propose a cross-modal global interaction and local alignment (GILA) approach for AVSR, which captures the deep audio-visual (A-V) correlations from both global and local perspectives. Specifically, we design a global interaction model to capture the A-V complementary relationship on modality level, as well as a local alignment approach to model the A-V temporal consistency on frame level. Such a holistic view of cross-modal correlations enable better multimodal representations for AVSR. Experiments on public benchmarks LRS3 and LRS2 show that our GILA outperforms the supervised learning state-of-the-art.
Sleep posture has been proven to be a crucial index for sleep monitoring in the Internet of Medical Things (IoMT). In this paper, an edge-computing system based on a smart mat for sleep posture recognition in IoMT is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. To meet the requirements of embedded system in IoMT, a light-weight algorithm that includes pre-processing, EdgeNet pre-training, model quantization, model deployment is proposed. Finally, the complete algorithm is deployed in embedded systems (STM32) and edge computing for sleep posture monitoring is implement in IoMT. Through a series of short-term and overnight experiments with 21 subjects, results exhibit that the accuracy of the short-term experiment is up to 92.10% and the overnight experiment is up to 75.43%. After quantization, the accuracy of the overnight is up to 74.79%, and the runtime of the complete algorithm is about 65ms in the STM32. Compared with other methods, edge-computing systems have the advantages of low power consumption, low cost, low latency, high reliability, and no risk of privacy leakage. With the promising results, the proposed system is capable of providing sleep posture recognition and can be integrated into IoMT as an edge device.
Objective
To observe the efficacy of oropharyngeal muscle exercise for relieving obstructive sleep apnea (OSAS) among stroke survivors.
Methods
Fifty stroke survivors with moderate OSAS were randomly divided into an observation group and a control group, each of 25. Both groups were given routine drugs and rehabilitation, while the observation group was additionally provided with oropharyngeal muscle exercises during the daytime for 20 minutes twice a day for 6 weeks. The control group received deep breathing therapy. Before and after the 6 weeks of treatment, both groups were evaluated using polysomnography. Their sleep quality and daytime sleepiness were measured using the Pittsburgh sleep quality index and the Stanford sleepiness scale. Any changes in the patients′ pharyngeal morphology after exercise were evaluated using MRI.
Results
After the oropharyngeal muscle exercises, the apnea hypoventilation index and minimum SaO2%, the snore index and sleep quality improved significantly. Daytime sleepiness was significantly relieved. Some structural remodeling of the pharyngeal airway was observed by MRI, including significantly larger retropalatal distance and shorter length of the soft palate. The retropalatal distance was positively and correlated with the duration of exercise while the length of soft palate correlated negatively.
Conclusion
Exercising the oropharyngeal muscles is a promising alternative treatment for stroke survivors with moderate OSAS. It improves the morphology of the oropharynx to relieve obstruction during sleeping.
Key words:
Apnea; Stroke; Oropharyngeal muscles; Polysomnography
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