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In telemedicine, an insecure and vulnerability medicine information system is important for nursing staff and patients. Relatively private information stolen by others still can be corrected. However, if attacked by hackers, mistakes in diagnosis can threaten the patient's life. In order to simplify the procedures in the authentication of telemedicine and to emphasize security in the patient's data, this paper proposes utilizing the symmetric key cryptography and the biometric authentication. We use a face eigenvector to insert the patient's physiological signals and to combine symmetric key cryptography thus protecting the integrity of the patient's data and improving the mode of operations.

This paper presents the design and development of a prototype for remote ECG data transmission based on Internet-enabled health care services and telemedicine fundamentals. An ECG acquisition system developed by the authors is used to acquire the ECG signal in lead-II configuration from patient and store it in .lvm format in a PC interfaced to patient module through RS232. This unit (data server) on the patient side then transfers the data to a remote client (on doctor's side) using TCP/IP as network protocol on LabVIEW 8.20 environment. Using this device, a specialist doctor can telematically move to the patient site and instruct medical personnel when handling a patient. During the last years, more and more modern tools have found their ways to different tasks during the design, the realization, and data processing in the area of Internet access to the e-health services. The telemedicine system demonstrated in this work is a combined real-time and store-and-forward facility.

In the present study, the important role of biomedical engineering (BME) professions in medical education (ME) is highlighted. We propose that BME and ME should be integrated in a joint department to provide high-quality occupational education in medicine. Then, we illustrate the basic connections and close relations between BME and ME with respect to the universal subfields of each scientific branch. As this regard, the proposed joint department in medical faculty would guide the medicine to use innovative educational tools such as humanistic models, realistic simulations, video games, web-based online resources, etc. in both basic and clinical ME. In addition, the combination of two disciplines would prepare the initiative facilities for multidisciplinary original research studies improving human health. Moreover, new trends in instructional evaluation could be captured by studying together in teams.

The ubiquity of broadband wireless coverage and widespread usage of smart phones around the world carry a potential for transforming health care services, reducing health care cost, and ensuring faster care for urgent cases. To these objectives, we present a mobile-based health monitoring solution that takes advantage of the mobile's increasing processing capability to address the rising cost of health care. The solution enables health care providers to easily analyze and diagnose a patient's data. This is possible due to the low cost in integrating a powerful data analysis tool with the mobile device. This paper presents a proof of concept that has been developed to monitor, record, and analyze the heart rate. The design enables a physician to develop custom analysis and monitoring to collect key indicators or set alerts without a need for infrastructure implementations to store or transfer the data.

The advent of cloud computing and the ubiquity of broadband wireless coverage and wide spread usage of smart phones around the world carries the potential for transforming health care services, reducing health care cost and ensuring faster care for urgent cases. To these objectives, we present a cloud-based mobile health monitoring solution that takes advantage of cloud infrastructure and mobile processing capability to address the rising cost of health care monitoring. The solution enables health care providers to remotely analyze, monitor and diagnose patient's data. The solution integrates a powerful data analysis tool, cloud computing and mobile services. This paper presents a proof of concept that has been developed to monitor, record and analyze heart rate. The design enables a physician to develop custom analysis and monitoring to collect key indicator or set alerts without a need for infrastructure implementations to store or transfer the data.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.