When the roads are monotonous, especially on the highways, the state of vigilance decreases and the state of drowsiness appears. Drowsiness is defined as the transitional phase from the awake to the sleepy state. However, In Morocco, the majority of fatal accidents on the highway are caused by drowsiness at the wheel, reaching 33.33% rate. Therefore, we proposed the conception and realization of an automatic method based on electroencephalogram (EEG) signals that can predict drowsiness in real time. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a personalized and optimized machine learning model (optimized decision tree classification method) under Python. The results are much significant and optimized, improving the accuracy from 95.7% to 96.4% and a time consuming from 0.065 to 0.053 seconds.
Several studies have shown that chest compressions (CC) alone may produce in addition to blood circulation, a short-term passive ventilation. However, it is not clear whether high CC quality may produce in even greater amount of ventilation volumes. The aim of this study was to evaluate whether CC, using a new feedback device, can produce a substantial and sustainable passive volumes compared to standard CC. Thirty inexperienced volunteers performed CC for 2 min on a developed thoracic lung model and using a new feedback device. Participants were randomized into two groups that performed either CC with feedback first, followed by a trial without feedback, or vice versa. Efficient compression rate (correct CC rate and depth simultaneously) was significantly higher in feedback session (43.6% versus 25.5%; P = 0.006). As well, CC rate and depth efficiency were improved with feedback. Moreover, average tidal volumes and minute volumes that occurred during CC alone were significantly improved in feedback session (79.8 ± 5 ml versus 72.9 ± 7 ml) and (8.8 l/min versus 7.9 l/min), respectively (P < 0.001). Yet, no significant difference was found between the first and the 90th second interval (9.04 l/min versus 8.68 l/min, P = 0.163) in the feedback session. Conversely, a significant difference was evident after the first 15th seconds interval without feedback (8.77 l/min initially versus 8.38 l/min; P = 0.041). This study revealed that the new CPR feedback device improved CC quality in inexperienced volunteers. As well, the passive ventilation volumes were significantly increased and sustained when the device was used.
<p>The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).</p>
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