Assisting Healthcare Using a Time-Series-Based Nonstationary Model for Smart Beds

Under any medical circumstance, the first and foremost requirement is to monitor physiological factors such as heart rate, blood pressure and oxygen level. Any breakdown in their coupling has been linked to ageing or illness. These physiological signals are nonstationary, and this paper analyses the transfer functions of nonstationary multidimensional time series of physiological signals. In this work, a method that integrates physiological modelling and functional elements into the smart bed for patients is proposed. This work includes experimentation on 10 smart bed patients. The proposed idea is validated and analysed to automatically capture any changes in the physiological signals due to postural changes, any impact of ageing or any requirement of a medical emergency. Next, it is demonstrated that the proposed method can be used to identify transient changes linked to medical emergencies for the given time-series data. These findings show the value of the proposed method in predicting the complicated vital-sign processes where conventional manual autoregulatory systems may fail in both healthy and pathological situations. The relation between the time series of physiological signals is an essential study field. A reliable time-varying model is presented to account for the possible nonstationarity of physiological data to determine the possibility of emergency care for patients. The suggested approach can identify variations and relations between signals because it is built as a dynamic model based on time-varying parameters. The technique used in this research includes readings of heart rate, blood pressure and oxygen level connected to the patient’s smart bed. The paper includes transient analysis and parametric evaluation as part of this work.