Collaborative systems for telemedicine diagnosis accuracy

Abstract

The transmission of medical data and the possibility for distant healthcare structures to share experiments about a given medical case raises several conceptual and technical questions. Good remote healthcare monitoring deals with more problems in personalized heath data processing compared to the traditional methods nowadays used in several parts of hospitals in the world. The adoption of telemedicine in the healthcare sector has significantly changed medical collaboration. However, to provide good telemedicine services through new technologies such as cloud computing, cloud storage, and so on, a suitable and adaptable framework should be designed. Moreover, in the chain of medical information exchange, between requesting agencies, including physicians, a secure and collaborative platform enhanced the decision-making process. This paper provides an in-depth literature review on the interaction that telemedicine has with cloud-based computing. On the other hand, the paper proposes a framework that can allow various research organizations, healthcare sectors, and government agencies to log data, develop collaborative analysis, and support decision-making. The electrocardiogram (ECG) and electroencephalogram EEG case studies demonstrate the benefit of the proposed approach in data reduction and high-fidelity signal processing to a local level; this can make possible the extracted characteristic features to be communicated to the cloud database.

Determining the location of patients in remote rehabilitation by means of a wireless network

“…Modern scientists are also actively considering the possibility of transferring data from body sensors of patients to remote cloud servers through access points [2]. Determining the patient's location near certain telecommunications equipment will facilitate remote monitoring of the patient in the interests of telemedicine.…”

On the trade-off between compression efficiency and distortion of a new compression algorithm for multichannel EEG signals based on singular value decomposition

“…Since its invention by the German psychiatrist Hans Berger almost a century ago [1], electroencephalography (EEG) has continuously evolved, becoming a powerful and extensively used method that allows measuring safely and noninvasively the spatiotemporal dynamics of the brain activity with a high temporal resolution in the range of milliseconds, which enables detecting rapid changes in the brain rhythms [2]. The brain rhythms are the periodic fluctuations of human EEG, which are associated with cognitive processes, physiological states, and neurological disorders [3].…”