Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers

Amiri, Parvin; Abbasi, Hamid; Derakhshan, Amin; Gharib, Behdad; Nooralishahi, Behrang; Mirzaaghayan, Mohamadreza

doi:10.1109/embc44109.2020.9175481

Cited by 1 publication

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The type of dominant features fed to the classifier in [17] depend on the size of the sliding window and the BFS entropy of the extracted data frames. Moreover, the 1D CNN model is among the most efficient methods, which underlines the stability of this model and suggests its good ability to generalize on various data sets [41], including medical data [33]. The accuracy of studies on the drivers' behavior was based on monitoring one or two signals; the accuracy ranged from 60% to 80% [42].…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we propose an approach to driver activity recognition using a one-dimensional convolutional neural network (1D CNN). This neural network model has proven its effectiveness in signal classification, yielding state-of-the-art results [32,33]. Because biological signals have non-linear characteristics, convolutional neural networks are an adequate choice, as they are precisely developed for recognizing non-linear patterns in the data [34].…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

et al. 2020

View full text Add to dashboard Cite

Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety.

show abstract