Advances in telemedicine technologies have offered clinicians greater levels of real-time guidance and technical assistance for diagnoses, monitoring, operations or interventions from colleagues based in remote locations. The topic includes the use of videoconferencing, mentorship during surgical procedures, or machine-to-machine communication to process data from one location by programmes running in another. This edited book presents a variety of technologies with applications in telemedicine, originating from the fields of biomedical sensors, wireless sensor networking, computer-aided diagnosis methods, signal and image processing and analysis, automation and control, virtual and augmented reality, multivariate analysis, and data acquisition devices. The Internet of Medical Things (IoMT), surgical robots, telemonitoring, and teleoperation systems are also explored, as well as the associated security and privacy concerns in this field. Topics covered include critical factors in the development, implementation and evaluation of telemedicine; surgical tele-mentoring; technologies in medical information processing; recent advances of signal/image processing techniques in healthcare; a real-time ECG processing platform for telemedicine applications; data mining in telemedicine; social work and telemental health services for rural and remote communities; applying telemedicine to social work practice and education; advanced telemedicine systems for remote healthcare monitoring; the impact of tone-mapping operators and viewing devices on visual quality of experience of colour and grey-scale HDR images; modelling the relationships between changes in EEG features and subjective quality of HDR images; IoMT and healthcare delivery in chronic diseases; and transform domain robust watermarking method using Riesz wavelet transform for medical data security and privacy.
Inspec keywords: medical signal processing; telemedicine; public administration; Internet of Things; medical image processing; wavelet transforms; data mining; image watermarking
Other keywords: remote community social work; tele-mental health services; grey-scale HDR images; transform domain robust watermarking method; medical information processing; medical data privacy; medical data security; Internet of Medical Things; viewing devices; colour HDR images; chronic diseases; surgical tele-mentoring; real-time telemedicine ECG-processing; tone-mapping operators; telemedicine development; visual quality; Riesz wavelet transform; EEG features; healthcare signal processing; social work practice; data mining
Subjects: Textbooks; Biology and medical computing; Digital signal processing; Biomedical communication; Biomedical measurement and imaging; General electrical engineering topics; Computer vision and image processing techniques; Image and video coding; Signal processing and detection; Knowledge engineering techniques; General and management topics; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Patient diagnostic methods and instrumentation
With increasing focus on the quality and safety of health care, telemedicine has been used as an important tool by health-care stakeholders to support and enhance timely, equitable and effective health service delivery. While the positive impacts of telemedicine are well recognized, operationalizing and integrating telemedicine within health service delivery continues to face numerous challenges. These wide-ranging challenges include issues with technology, integrating with existing, and access to new, infrastructure, legislative and organizational requirements, stakeholder expectations, change management (as telemedicine is often offered as a new model of care) and financial and business considerations. Without explicit recognition of, and targeted strategies for, addressing these challenges, any telemedicine initiative risks not fulfilling its full potential. Similarly, improvements in telemedicine technologies should be complemented by recognition for and understanding of what works at the health service delivery level. The aim of the chapter is to provide a practical resource for stakeholders in telemedicine that outlines the critical factors that are essential in the development, implementation and evaluation of telemedicine. Using a case-study approach, which will draw on a practical example from the South Australian health-care sector, this chapter brings together evidence from the literature as well as experiences from the frontline of telemedicine. While there is no `one size fits all' approach, this chapter will showcase the critical factors that have been demonstrated to influence the success and sustainability of telemedicine services.
Surgical tele-mentoring is a model within the broad discipline of telemedicine that involves the use of information technology to provide real-time guidance and technical assistance for surgical procedures from an expert surgeon at a different location. This chapter addresses the history and progression, applications, challenges, limitations, and future directions of surgical tele-mentoring as a means to distribute advanced surgical expertise around the world.
Providing healthcare is a multidisciplinary effort where Information plays a lifesaving role. Advances in health technologies have diversified the sources of information that can be analyzed and used for correct diagnostics and decision-making. Researchers and system developers in the field of telemedicine are consciously inventing and improving data generation, collection, and processing methods with a view to improving the quality of care and possibly reduce cost and effort need in providing care. Bringing newly developed technologies specific to information processing to the attention of healthcare providers (individuals or healthcare institutes) is vitally important to improve the quality of care. With this in mind, in this chapter the authors explain the importance of a number of health indicators that can be measured for data collection purposes such as temperature, heart rate, blood pressure, and respiration rate. This chapter also presents several latest technologies that capture data and process it to produce meaningful and visual information that can be used for diagnostics and treatment purposes such as X-ray, computerized tomography (CT) scan, magnetic resonance imaging (MRI) and ultrasound. The chapter highlights the importance of data mining techniques in retrieving the required information from an information-pool in providing the right healthcare. The authors emphasize the fact that retrieving the required information may not be enough to provide the right healthcare service since the retrieved information needs to be interpreted by healthcare professionals to resolve a healthcare case.
Of late, there has been a great deal of exploration and scope in the development of computer-oriented visuals and image processing in healthcare sectors. Despite timely expansions in this technological arena over the years, there is a lack of progress in explorations, which requires revived concentration. Based on this need, healthcare diagnosis through image processing and computer-oriented visuals with the help of sensors is promoted [48]. This approach involves integration of cues and modalities of images and visuals, which enhances the performance of processing the visuals and images. This chapter deals with the latest and the recent advancements in signal/image processing techniques, and network design in healthcare applications. Further, the study aims at academic and scientific contributions to the fellow researchers of this emerging area of technology. Also, the viability/ compatibility among electrophysiological signals such as ECG, EMG, EEG along image processional functionalities have been identified. In addition, some real case studies such as neuroscience, cardiovascular system have been discussed.
This study focuses on the development of an efficient method by combining the feature extraction and classification methods and their implementation on the microcontroller test platform. The cosine Stockwell transform (CST) is used for extracting the significant amount of information from the corresponding ECG signals in lower dimensions. These features represent each of the ECG signals and are further identified using PSO-tuned twin support vector machines (TSVMs) into their different categories. The proposed method is implemented on the 32-bit advanced RISC machine (ARM) platform. The platform is validated on the benchmark MIT-BIH arrhythmia data generated in real time and evaluated under category-oriented analysis scheme. The platform is integrated with the Wi-Fi module which sends the information of classified outputs to a remote platform. Once an abnormality is detected by the platform, a pop-up message can be viewed on the displaying module interfaced with the platform which behaves as an alarm. The platform reported an accuracy of 95.8% in the category-oriented assessment scheme. Such type of prototyping of proposed method on hardware platforms deliver an assistive diagnostic solution to the users and should be employed in hospitals for cardiovascular disease diagnosis by providing an enriched platform capable of performing real-time diagnosis for telemedicine applications.
To date, the field of telemedicine is at a critical standpoint and faces a wide variety of challenges. Voluminous data are generated through the interaction among the telemedicine stakeholders, which are ever increasing. It is well conjectured that the successful implementation of telemedicine largely depends on the effective and efficient knowledge extraction from this available data cloud. However, due to lack of proper integration of the data mining techniques, the stakeholders are not getting the full-fledged benefit from this promising platform. Considering the aforementioned fact, this book chapter provides a contrivance to integrate data mining techniques into telemedicine connecting all the stakeholders into a single podium using data engine. It illustrates the prospects of different data mining techniques and their integration for telemedicine. These techniques combine all the basic classification and clustering method including the state-of-the-art artificial neural network (ANN) and deep learning procedure for disease prediction. Two case studies, heart diseases, and breast cancer prediction have been demonstrated applications of the integrated data mining engine.
Rural and remote communities often have complex and diverse mental health needs and inadequate mental health services and infrastructure. Information and communication technologies provide an array of means for connecting rural and remote communities to specialist mental health practitioners as used in psychiatry and psychology. Social work practitioners have additional skills to bring to tele-mental health and in particular, the socio-cultural dimensions that impact on mental health and therefore the ability to recognize and explore these with participants, as well as, refer participant to resources and services outside of or in addition to psychology or medical fields. However, despite this potential, a review of international literature reveals that information and communication technologies (ICTs) have not attained widespread uptake in social work practice in rural communities. This chapter reviews the social work literature on ICTs, the tele-psychology and psychiatry literature and provides suggestions on how to enhance engagement with ICT by social workers to implement and provide social work services tailored to rural and remote community needs, values and preferences.
Tele-social work has the potential to broaden the scope of social work practice by improving the accessibility and flexibility of social work services to individuals and groups. This chapter discusses the creative ways in which tele-social work is being implemented, both in Australian and international contexts; challenges and barriers experienced by practitioners, educators, and students. It adds to the knowledge of teaching tele-social work in a classroom setting to build students' confidence and competence in use of technology for social work practice. It proposes to have a cultural shift in academia towards up skilling undergraduate students in using technology for practice purposes.
In this chapter, an advanced telemedicine system has been presented for monitoring remotely located patients suffers from various types of diseases. Telemedicine is a multidisciplinary field, which needs expert physician and staff, which also include high cost and types of equipment to provide quality service to patients. As telemedicine systems are now capable of monitoring remote patients with greater efficiency using numbers of advance computational methods, and researchers showing great interest in this field. Telemedicine systems may support to deal with various types of biomedical signals like EEG (electroencephalography), ECG (electrocardiography), EMG (electromyography), etc. along with the integration of modern techniques. This chapter also deals with standards uses by telemedicine system along with its special features. A cloud-based workflow model for monitoring of remote patients has been proposed here. For the continuous monitoring and analysis of biomedical signals obtained from remote patients, telemedicine system may play a great role for technical, social, and cultural development of society. Here, a discussion has been made on various aspects of the monitoring system and different issues with latest development and improvement in this field.
Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.
Quality of experience (QoE) is a human-centric paradigm, which produces the blueprint of human-behavioral-states such as perception, emotion, cognition, and expectation. Recent advances in neurophysiological monitoring tools have facilitated the study of frequency, time, and location of neuronal activity to an unprecedented degree, as well as opened doors to a better understanding of human overall behavioral systems. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This chapter proposes a novel objective QoE model for high dynamic range (HDR) images and is based on the relationship between objective (i.e. delta-beta coupling) and subjective measures (i.e. mean opinion score MOS). The analysis of the results indicate that the proposed QoE model has a strong correlation with MOS scores, hence can be effectively used in predicting the overall HDR image quality. An advantage of the model is that it is lightweight and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization. Future mobile providers can benefit from applying the proposed QoE-based model to optimize users' acceptability and satisfaction for different HDR image scenarios.
Digital health broadly incorporates categories such as mobile health (mHealth), information technology comprising electronic health records, reimbursements (IT), wearable devices, telehealth and telemedicine. Recent advancements on this front includes Internet of Things (IoT) ecosystem, which provides a connected ecosystem for flawless information flow within various technologies involved at hardware, software and networking layer. These enabling technologies include devices embedded with sensor, actuator and communication protocol, which transmits and receives the data in real time. Along with other end applications such as smart energy transmission, smart homes, intelligent logistics and smart towns, healthcare provides an attractive opportunity area for successful implementation. Current estimates predict that nearly 60% of organizations a have implemented IoT in healthcare industry in partial or complete form, to deliver value to patients and transition from disjoint and reactive model towards interoperable and proactive service delivery model. Internet of medical things (IoMT)-enabled machine-to-machine interaction between devices in patient's body environment with enabling architecture, is predicted to provide higher impact in chronic disease care. Further, current topic would broadly review clinician side transformations of technology, explaining how IoT applications would create value for patients in different scenario and its relevance in clinical settings.
Rapid progress of digital multimedia content has tremendously improved different forms of information and its processing. New digital technologies help to store, transmit and process the information in a quick and accurate way. However, digital data in the form of videos and images can be effortlessly manipulated and redistributed using the computers. Violation of ownership rights and verifying the integrity of the digital content can be significantly improved by the watermarking approaches. Watermarking algorithms embed additional information for verifying the authenticity and trustworthiness. Security and privacy of medical data in the form of image or other 1-D signals are of prime importance and has become an emerging area in the field of biomedical information technology. Biomedical image watermarking algorithms enable transmission of medical records and patient history in a secure way. The aim of this book chapter is to propose a new watermarking approach using a transform domain for medical image security. Riesz wavelet transform (RWT) and singular value decomposition (SVD) is employed for embedding the watermarking in the cover medical image at the transmitter side. At the receiver, the embedded information is recovered successfully using the watermarking extraction algorithm. The RWT-SVD algorithm is tested on different types of medical images like X-ray, CT scan, MRI and retinal images. The watermark is extracted at the receiver without the original image. Imperceptibility evaluation using several metrics (SNR, PSNR, WPSNR, SSIM, MSSIM, SC) shows the improved performance of the proposed approach. In addition to this, robustness analysis is also carried in terms of correlation coefficient.
This book presents new telemedicine approaches for healthcare data analysis and diagnosis of medical conditions. Telemedicine technologies play an important role in the fields of biomedical sensors, wireless body sensor networking, computer aided diagnosis methods, signal and image processing, and analysis, automation and control, virtual and augmented reality, multivariate analysis and data acquisition devices.