Systems capable of monitoring the biological condition of a driver and issuing warnings during instances of drowsiness have recently been studied. Moreover, many researchers have reported that biological signals, such as brain waves, pulsation waves, and heart rate, are different between people who have and have not consumed alcohol. Currently, we are developing a noninvasive system to detect individuals driving under the influence of alcohol by measuring biological signals. We used the frequency time series analysis to attempt to distinguish between normal and intoxicated states of a person as the basis of the sensing system.
in this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. the distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. the performance of the proposed method was tested using a dataset provided by physionet and the University of tokyo Hospital. the results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%. Biometric information monitoring devices are used in various clinical scenarios such as surgeries and the intensive care units (ICUs) 1. Many of these devices raise an alarm when clinical deterioration of the patient (detected via biological indices) is detected. For example, a pulse oximeter, which is capable of measuring the saturation of peripheral oxygen (SpO 2) through a simple pinch on the fingertip, raises an alarm when SpO 2 is below the threshold value (Generally 89-92% 2). In other cases, blood pressure (e.g., diastolic blood pressure) can be continuously measured using a sphygmomanometer, and an alert is sounded when it falls below a set threshold. The thresholds for many of these alarms are set based on prior experiences of the healthcare provider and the patient's condition 3. These medical devices can perform long-term monitoring of the patients' biological information and are important for efficient and effective treatment. However, conventional medical devices raise an alarm only after detecting a deterioration. This proves to be problematic for the medical staff, who cannot stay near the patient all the time. To solve this problem, several studies have proposed clinical deterioration prediction systems 4,5. For example, Langley et al. 4 focused on the change of heart rate intervals and they proposed an approach to predict the development of idiopathic atrial fibrillation with an accuracy of 56.0%. This approach used deviance from the average heart rate interval as a predictor. Lynn and Chiang 6 proposed an algorithm based on nonlinear features
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