A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology.

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.

A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals.

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.

Ensemble neural network approach detecting pain intensity from facial expressions.

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.

An evaluation of the benefits and challenges of video consulting between general practitioners and residential aged care facilities.

This research evaluated a project that provided video consultations between general practitioners (GPs) and residential aged care facilities (RACFs), with the aim of enabling faster access to medical care and avoidance of unnecessary hospital transfers. GPs were paid for video consultations at a rate equivalent to existing insurance reimbursement for supporting telehealth services. Evaluation data were gathered by direct observation at the project sites, semi-structured interviews and video call data from the technical network. Three pairs of general practices and RACFs were recruited to the project. 40 video consultations eligible for payment occurred over a 6 month period, three of which were judged to have avoided hospital attendance. The process development and change management aspects of the project required substantially more effort than was anticipated. This was due to problems with RACF technical infrastructure, the need for repeated training and awareness raising in RACFs, the challenge of establishing new clinical procedures, the short length of the project and broader difficulties in the relationships between GPs and RACFs. Video consulting between GPs and RACFs was clinically useful and avoided hospital attendance on a small scale, but further focus on process development is needed to embed this as a routine method of service delivery.

The costs and benefits of technology-enabled, home-based cardiac rehabilitation measured in a randomised controlled trial.

We conducted a cost benefit analysis of a home telehealth-based cardiac rehabilitation programme compared to the standard hospital-based programme. A total of 120 participants were enrolled in a trial, with 60 randomised to the telehealth group and 60 randomised to usual care. Participants in the telehealth group received a mobile phone, Wellness Diary and a Wellness web portal, with daily text messaging. Participants in the usual care group received the standard 6-week hospital-based outpatient cardiac rehabilitation programme, including gym sessions. The cost of delivery by telehealth was slightly lower than for patients attending a rehabilitation service in person. From the provider's perspective, the telehealth intervention could be delivered for $1633 per patient, compared to $1845 for the usual care group. From the participant's perspective, patient travel costs for home rehabilitation were substantially less than for hospital attendance ($80 vs $400). Cardiac rehabilitation by telehealth offers obvious advantages and the option should be available to all patients who are eligible for cardiac rehabilitation.

Care assessment platform: an ICT-enabled home care model for secondary prevention of cardiovascular diseases.

BACKGROUND: Cardiovascular disease (CVD) is the leading chronic diseases affecting developed countries. Traditional approach to secondary prevention of CVD through hospital-based cardiac rehabilitation (CR) is hampered by the lack of uptake and adherence.

Service and business model for technology enabled and home-based cardiac rehabilitation programs.

Cardiac rehabilitation programs are comprehensive life-style programs aimed at preventing recurrence of a cardiac event. However, the current programs have globally significantly low levels of uptake. Home-based model can be a viable alternative to hospital-based programs. We developed and analysed a service and business model for home based cardiac rehabilitation based on personal mentoring using mobile phones and web services. We analysed the different organizational and economical aspects of setting up and running the home based program and propose a potential business model for a sustainable and viable service. The model can be extended to management of other chronic conditions to enable transition from hospital and care centre based treatments to sustainable home-based care.