Correction for apparent prolongation of mean transit time resulting from response time in a thermodilution system

MEASURING CARDIAC OUTPUT THROUGH THERMODILUTION BASED ON MACHINE LEARNING

Cardiac output (CO) refers to the amount of blood ejected from a unilateral ventricle per minute and is an important measure of cardiac function. Thermodilution is the gold standard for CO measurement because of its accuracy. However, the traditional thermodilution method requires calibration of the correction factor before measurement, which makes its practical application difficult. Therefore, conducting CO measurement by using a machine-learning-based thermodilution method is proposed in this paper, and CO is regressed and predicted through the thermodilution curve by a machine learning model. In this paper, we constructed five cardiac vascular models, and three of them were randomly selected to simulate the thermodilution process. Nine features of the thermodilution curve from the time–frequency domains were extracted and fed into the multilayer perceptron model for training. On the basis of a cross-validation method, the accuracy of the final prediction model was 97.99% ([Formula: see text]%). Simultaneously, a trained neural network was used to predict the CO of the remaining two cardiac vascular models, and the resulting error was within 5%. In this paper, an experimental system consisting of a water pump, a three-way valve and a temperature sensor is also designed, and the thermodilution curves at different quantities of flow are tested and regressed and predicted with the above model, with the error being within 10%, which met the requirement for real-world use, and thus, a method was established for measuring CO by using machine-learning-based thermodilution.

FFBPNN-BASED HIGH DROWSINESS CLASSIFICATION USING EMG AND WPT

The detection of drowsiness level is important because it is the main reason for fatal road accidents. Electromyography of the upper arm and shoulder is an important physiological signal affected by drivers’ drowsiness, in which its amplitude level and frequency band of the sleep-deprived case are different than those of the alert state. Therefore depending on electromyography (EMG), its drowsiness frequency (80–100[Formula: see text]Hz) was detected in order to determine high drowsiness state based on wavelet packet transform (WPT) which decomposes the EMG signal into its approximation and detail coefficients up to level 4 using db2, db7, sym5 and coif5 wavelets. In this research after extraction, the two higher order statistical features, kurtosis and skewness, are computed from 3[Formula: see text]s window of the three EMG channels, and analysis of variance test is used to check whether their mean values are different for the different classes as both [Formula: see text]-values are less than 0.005 under db2 wavelet. Therefore, they were supplied to feed forward back propagation neural network (FFBPNN) as this type of neural network is used for distinguishing and classification purposes for different objects. They obtained an accuracy of 75% for detecting high levels among other levels of normal and low drowsiness with an average sensitivity of 78.63% and specificity of 75.97% because the spectrum of the EMG alert (non-drowsiness) signal of 80–100 Hz is different from that of drowsy 80–90[Formula: see text]Hz and high drowsy 78–95[Formula: see text]Hz signals.