Clinical assessment of wireless ECG transmission in real-time cardiac telemonitoring

Performance and Usability Evaluation of an Extended Reality Platform to Monitor Patient’s Health during Surgical Procedures

An extended-reality (XR) platform for real-time monitoring of patients’ health during surgical procedures is proposed. The proposed system provides real-time access to a comprehensive set of patients’ information, which are made promptly available to the surgical team in the operating room (OR). In particular, the XR platform supports the medical staff by automatically acquiring the patient’s vitals from the operating room instrumentation and displaying them in real-time directly on an XR headset. Furthermore, information regarding the patient clinical record is also shown upon request. Finally, the XR-based monitoring platform also allows displaying in XR the video stream coming directly from the endoscope. The innovative aspect of the proposed XR-based monitoring platform lies in the comprehensiveness of the available information, in its modularity and flexibility (in terms of adaption to different sources of data), ease of use, and most importantly, in a reliable communication, which are critical requirements for the healthcare field. To validate the proposed system, experimental tests were conducted using instrumentation typically available in the operating room (i.e., a respiratory ventilator, a patient monitor for intensive care, and an endoscope). The overall results showed (i) an accuracy of the data communication greater than 99 %, along with (ii) an average time response below ms, and (iii) satisfying feedback from the SUS questionnaires filled out by the physicians after intensive use.

Fog-based healthcare systems: A systematic review

The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help physicians to consider patients' needs in healthcare system designing and offering service, which leads to an increase in patient satisfaction. Therefore, healthcare is becoming a growing contesting market. With this significant growth in healthcare systems, such challenges as huge data volume, response time, latency, and security vulnerability are raised. Therefore, fog computing, as a well-known distributed architecture, could help to solve such challenges. In fog computing architecture, processing components are placed between the end devices and cloud components, and they execute applications. This architecture is suitable for such applications as healthcare systems that need a real-time response and low latency. In this paper, a systematic review of available approaches in the field of fog-based healthcare systems is proposed; the challenges of its application in healthcare are explored, classified, and discussed. First, the fog computing approaches in healthcare are categorized into three main classes: communication, application, and resource/service. Then, they are discussed and compared based on their tools, evaluation methods, and evaluation metrics. Finally, based on observations, some open issues and challenges are highlighted for further studies in fog-based healthcare.

A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky⁻Golay Filter for ECG Denoising

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.

Significance of modified empirical mode decomposition for ECG denoising

The primary objective of the presented work is to exploit the power of modified empirical mode decomposition (M-EMD) for the denoising of ECG signals. It is well known that the ECG signals get corrupted by a number of noises during the recording process. Especially, during wireless ECG recording and ambulatory patient monitoring, the signal gets corrupted by additive white Gaussian noise (AWGN). Over the years, several techniques have been proposed for ECG denoising. Among those, empirical mode decomposition (EMD) and nonlocal means (NLM) algorithm are noted to be quite effective. Further, the NLM-based approach is better in retaining the morphological characteristics in comparison to the EMD. Consequently, the two approaches are effectively combined in this paper so that each one complements the other. In the proposed approach, the noisy ECG signal is first preprocessed using the NLM algorithm. This is followed by decomposition of the partially denoised output through M-EMD. The decomposed components are suitably thresholded and then reconstructed to obtain the final denoised signal. This largely addresses the issue of under-averaged regions noted in the case of NLM-based denoising. Furthermore, the proposed approach is noted to be superior to the other existing techniques.

Simulation of an optimized technique based on DS-CDMA for simultaneous transmission of multichannel biosignals

Telemedicine is becoming increasingly, with applications in many areas of healthcare, such as home telecare of the elderly, diagnosis at a distance and robotic surgery. The simultaneous transmission of several leads of biomedical signals should be considered in telemedicine, given the many benefits it brings. Code division multiple access (CDMA) is a multiple access technique that enables users to transmit independent information simultaneously within the same bandwidth. The direct sequence CDMA (DS-CDMA) is a variant of the CDMA technique in which a pseudorandom sequence having a higher bandwidth than the information signal is used to modulate the information signal directly. Biomedical signals are confidential; thus, their transmission must be secured. In this paper we propose a protocol similar to DS-CDMA for the simultaneous transmission of all of the leads of some multichannel biomedical signals. We assigned orthogonal codes to different leads of a signal. The convolution of each lead with the code gives a signal spread over a broad frequency band. All of the spread signals are then mixed to produce a single composite signal. This composite signal is frequency modulated, amplified and transmitted. At the reception, inverse functions to the previous are developed to perform demodulation, demultiplexing and extraction of the physiological signals transmitted. We used the discrete Walsh functions as codes. The results obtained are satisfactory, even in situations where the noise disturbances are significant.

Ubiquitous Emergency Medical Service System Based on Wireless Biosensors, Traffic Information, and Wireless Communication Technologies: Development and Evaluation

This study presents a new ubiquitous emergency medical service system (UEMS) that consists of a ubiquitous tele-diagnosis interface and a traffic guiding subsystem. The UEMS addresses unresolved issues of emergency medical services by managing the sensor wires for eliminating inconvenience for both patients and paramedics in an ambulance, providing ubiquitous accessibility of patients' biosignals in remote areas where the ambulance cannot arrive directly, and offering availability of real-time traffic information which can make the ambulance reach the destination within the shortest time. In the proposed system, patient's biosignals and real-time video, acquired by wireless biosensors and a webcam, can be simultaneously transmitted to an emergency room for pre-hospital treatment via WiMax/3.5 G networks. Performances of WiMax and 3.5 G, in terms of initialization time, data rate, and average end-to-end delay are evaluated and compared. A driver can choose the route of the shortest time among the suggested routes by Google Maps after inspecting the current traffic conditions based on real-time CCTV camera streams and traffic information. The destination address can be inputted vocally for easiness and safety in driving. A series of field test results validates the feasibility of the proposed system for application in real-life scenarios.

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.

An adaptive framework for real-time ECG transmission in mobile environments

Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality.

Predictable and reliable ECG monitoring over IEEE 802.11 WLANs within a hospital

Telecardiology provides mobility for patients who require constant electrocardiogram (ECG) monitoring. However, its safety is dependent on the predictability and robustness of data delivery, which must overcome errors in the wireless channel through which the ECG data are transmitted. We report here a framework that can be used to gauge the applicability of IEEE 802.11 wireless local area network (WLAN) technology to ECG monitoring systems in terms of delay constraints and transmission reliability. For this purpose, a medical-grade WLAN architecture achieved predictable delay through the combination of a medium access control mechanism based on the point coordination function provided by IEEE 802.11 and an error control scheme based on Reed-Solomon coding and block interleaving. The size of the jitter buffer needed was determined by this architecture to avoid service dropout caused by buffer underrun, through analysis of variations in transmission delay. Finally, we assessed this architecture in terms of service latency and reliability by modeling the transmission of uncompressed two-lead electrocardiogram data from the MIT-BIH Arrhythmia Database and highlight the applicability of this wireless technology to telecardiology.

Smart health monitoring systems: an overview of design and modeling

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

GPS and GPRS Based Telemonitoring System for Emergency Patient Transportation

Telemonitoring during the golden hour of patient transportation helps to improve medical care. Presently there are different physiological data acquisition and transmission systems using cellular network and radio communication links. Location monitoring systems and video transmission systems are also commercially available. The emergency patient transportation systems uniquely require transmission of data pertaining to the patient, vehicle, time of the call, physiological signals (like ECG, blood pressure, a body temperature, and blood oxygen saturation), location information, a snap shot of the patient, and voice. These requirements are presently met by using separate communication systems for voice, physiological data, and location that result in a lot of inconvenience to the technicians, maintenance related issues, in addition to being expensive. This paper presents design, development, and implementation of such a telemonitoring system for emergency patient transportation employing ARM 9 processor module. This system is found to be very useful for the emergency patient transportation being undertaken by organizations like the Emergency Management Research Institute (EMRI).

Enhanced protocol for real-time transmission of echocardiograms over wireless channels

This paper presents a methodology to transmit clinical video over wireless networks in real-time. A 3-D set partitioning in hierarchical trees compression prior to transmission is proposed. In order to guarantee the clinical quality of the compressed video, a clinical evaluation specific to each video modality has to be made. This evaluation indicates the minimal transmission rate necessary for an accurate diagnosis. However, the channel conditions produce errors and distort the video. A reliable application protocol is therefore proposed using a hybrid solution in which either retransmission or retransmission combined with forward error correction (FEC) techniques are used, depending on the channel conditions. In order to analyze the proposed methodology, the 2-D mode of an echocardiogram has been assessed. A bandwidth of 200 kbps is necessary to guarantee its clinical quality. The transmission using the proposed solution and retransmission and FEC techniques working separately have been simulated and compared in high-speed uplink packet access (HSUPA) and worldwide interoperability for microwave access (WiMAX) networks. The proposed protocol achieves guaranteed clinical quality for bit error rates higher than with the other protocols, being for a mobile speed of 60 km/h up to 3.3 times higher for HSUPA and 10 times for WiMAX.