Titlebook: Rhythms in Healthcare; M. Shamim Kaiser,Mufti Mahmud,Shamim Al Mamun Book 2022 The Editor(s) (if applicable) and The Author(s), under excl

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Rhythmic Pattern of EEG for Identifying Schizophrenia,in abnormalities in SZ. However, most existing studies are based on functional connectivity (FC) of brain regions under an implicit assumption of stationary of FC throughout the time. Recent studies have shown that a human brain has more dynamic behaviour rather than stationary. In this paper, we ex

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,Prior Prediction and Management of Autism in Child Through Behavioral Analysis Using Machine LearniChild prediction will be more beneficial if possible to predict autism in the preliminary stage and this research will be helpful for physicians, parents, and doctors before the formal screening test. On the other hand, existing screening tools for early prediction of autism involve long-term observ

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DNN and LiDAR Sensor Based Crowd Avoidance Method for Nurse-Following Robot in Healthcare,der two things. First, we should track the target person in the real environment and then should avoid the crowd or obstacles. In this chapter, we showed that our robot can track a target nurse using a color histogram matching algorithm and can follow that target nurse. We also showed that if the nu

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Investigation on Heart Attack Prediction Based on the Different Machine Learning Approaches,g (ML) based algorithm can find a pattern among different health-related parameters and provide an accurate prediction of a heart attack. This paper investigates various ML algorithms which suit most in predicting heart attacks on the Cleveland dataset. Due to missing values, three features were dro

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Wearable Devices for Monitoring Vital Rhythm and Earlier Disease Diagnosis of Treatment,dly as a result of the demand for point-of-care (POC) diagnosis and real-time observation of long-term health issues. Nowadays to resolve this issue wearable devices have been spread among mass people for medical diagnostics, neurobiological health monitoring, and evaluation, The amazing advancement

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DNN and LiDAR Sensor Based Crowd Avoidance Method for Nurse-Following Robot in Healthcare,the LiDAR sensor to calculate the distance. For crowd avoidance, we use the line following and circle following method. Assuming that the robot was blocked from the target nurse in various situations and all the situations our robot successfully avoids the crowd. Our result confirms that the robot can avoid the crowd successfully.

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