Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning

Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve 2.73 ± 2.36, while the MAE was 7.73 ± 3.81 using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.

An improved method for recognizing pediatric epileptic seizures based on advanced learning and moving window technique

In this work, advanced learning and moving window-based methods have been used for epileptic seizure detection. Epilepsy is a disorder of the central nervous system and roughly affects 50 million people worldwide. The most common non-invasive tool for studying the brain activity of an epileptic patient is the electroencephalogram. Accurate detection of seizure onset is still an elusive work. Electroencephalogram signals belonging to pediatric patients from Children’s Hospital Boston, Massachusetts Institute of Technology have been used in this work to validate the proposed method. For determining between seizure and non-seizure signals, feature extraction techniques based on time-domain, frequency domain, time-frequency domain have been used. Four different methods (decision tree, random forest, artificial neural network, and ensemble learning) have been studied and their performances have been compared using different statistical measures. The test sample technique has been used for the validation of all seizure detection methods. The results show better performance by random forest among all the four classifiers with an accuracy, sensitivity, and specificity of 91.9%, 94.1%, and 89.7% respectively. The proposed method is suggested as an improved method because it is not channel specific, not patient specific and has a promising accuracy in detecting epileptic seizure.

Attention-based Graph ResNet with focal loss for epileptic seizure detection

Epilepsy is a chronic brain disease resulted from the central nervous system lesion, which leads to repeated seizure occurs for the patients. Automatic seizure detection with Electroencephalogram (EEG) has witnessed great progress. However, existing methods paid little attention to the topological relationships of different EEG electrodes. Latest neuroscience researches have demonstrated the connectivity between different brain regions. Besides, class-imbalance is a common problem in EEG based seizure detection. The duration of epileptic EEG signals is much shorter than that of normal signals. In order to deal with the above mentioned two challenges, we propose to model the multi-channel EEG data using the Attention-based Graph ResNet (AGRN). In particular, each channel of the EEG signal represents a node of the graph and the inter-channel relations are modeled via the adjacency matrix in the graph. The loss function of the ARGN model is re-designed using focal loss to cope with the class-imbalance problem. The proposed ARGN with focal model could learn discriminative features from the raw EEG data. Experiments are carried out on the CHB-MIT dataset. The proposed model achieves an average accuracy of 98.70%, a sensitivity of 97.94%, a specificity of 98.66% and a precision of 98.62%. The Area Under the ROC Curve (AUC) is 98.69%.

Emotion-sensitive voice-casting care robot in rehabilitation using real-time sensing and analysis of biometric information

An important part of nursing care is the physiotherapist’s physical exercise recovery training (for instance, walking), which is aimed at restoring athletic ability, known as rehabilitation (rehab). In rehab, the big problem is that it is difficult to maintain motivation. Therapies using robots have been proposed, such as animalistic robots that have positive psychological, physiological, and social effects on the patient. These also have an important effect in reducing the on-site human workload. However, the problem with these robots is that they do not actually understand what emotions the user is currently feeling. Some studies have been successful in estimating a person’s emotions. As for non-cognitive approaches, there is an emotional estimation of non-verbal information. In this study, we focus on the characteristics of real-time sensing of emotion through heart rates – unconsciously evaluating what a person experiences – and applying it to select the appropriate turn of phrase by a voice-casting robot. We developed a robot to achieve this purpose. As a result, we were able to confirm the effectiveness of a real-time emotion-sensitive voice-casting robot that performs supportive actions significantly different from non-voice casting robots.

Caregiver development of activity-supporting services for smart homes

Older adults often need some level of assistance in performing daily living activities. Even though these activities are common to the vast majority of individuals (e.g., eating, bathing, dressing), the way they are performed varies across individuals. Supporting older people in performing their everyday activities is a major avenue of research in smart homes. However, because of its early stage, this line of work has paid little attention on customizing assistive computing support with respect to the specific needs of each older adult towards improving its effectiveness and acceptability. We propose a tool-based approach to allowing caregivers to define services in the area of home daily living, leveraging their knowledge and expertise on the older adult they care for. This approach consists of two stages: 1) a wizard allows caregivers to define an assistive service, which supports aspects of a daily activity that are specific to an older adult; 2) the wizard-generated service is uploaded in an existing smart home platform and interpreted by a dedicated component, carrying out the caregiver-defined service. Our approach has been implemented. Our wizard has been successfully used to define existing manually-programmed, activity-supporting services. The resulting services have been deployed and executed by an existing assisted living platform deployed in the home of community-dwelling individuals. They have been shown to be equivalent to their manually-programmed counterparts. We also conducted an ergonomics study involving five occupational therapists, who tested our wizard with clinical vignettes describing fictitious patients. Participants were able to successfully define services while revealing an ease of use of our wizard.