scholarly journals A real-time wireless wearable electroencephalography system based on Support Vector Machine for encephalopathy daily monitoring

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877956 ◽  
Author(s):  
Qing Zhang ◽  
Pingping Wang ◽  
Yan Liu ◽  
Bo Peng ◽  
Yufu Zhou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Early Warning ◽  
Mobile Terminal ◽  
Support Vector ◽  
Brain Health ◽  
Intelligent Support ◽  
Daily Monitoring ◽  
Term Monitoring

Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for wearing, inconvenient for retrieving the recordings by patients themselves, unable to timely provide accurate classification, and early warning information. Therefore, we proposed a wireless wearable electroencephalography system for encephalopathy daily monitoring, named as Brain-Health, which focused on the following three points: (a) the monitoring device integrated with electroencephalography acquisition sensors, signal processing chip, and Bluetooth, attached to a sport hat or elastic headband; (b) the mobile terminal with dedicated application, which is not only for continuous recording and displaying electroencephalography signal but also for early warning in real time; and (c) the encephalopathy’s classification algorithm based on intelligent Support Vector Machine, which is used in a new application of wearable electroencephalography for encephalopathy daily monitoring. The results showed a high mean accuracy of 91.79% and 93.89% in two types of classification for encephalopathy. In conclusion, good performance of our Brain-Health system indicated the feasibility and effectiveness for encephalopathy daily monitoring and patients’ health self-management.

scholarly journals Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Bin Ma ◽  
Zhaolong Wu ◽  
Shengyu Li ◽  
Ryan Benton ◽  
Dongqi Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Internet Of Things ◽  
Real Time ◽  
Early Warning ◽  
Smart Phone ◽  
Physiological Parameters ◽  
Classification Model ◽  
Support Vector ◽  
Obstructive Sleep

Abstract Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

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