Anpeng Huang

Differential Modulation for Bidirectional Relaying With Analog Network Coding

In this correspondence, we propose an analog network coding scheme with differential modulation (ANC-DM) using amplify-and-forward protocol for bidirectional relay networks when neither the source nodes nor the relay knows the channel state information (CSI). The performance of the proposed ANC-DM scheme is analyzed and a simple asymptotic bit error rate (BER) expression is derived. The analytical results are verified through simulations. It is shown that the BER performance of the proposed differential scheme is about 3 dB away from that of the coherent detection scheme. To improve the system performance, the optimum power allocation between the sources and the relay is determined based on the simplified BER. Simulation results indicate that the proposed differential scheme with optimum power allocation yields 1-2 dB performance improvement over an equal power allocation scheme.

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

Design and tests of a smartphones-based multi-lead ECG monitoring system

With the rapid development of wireless communications and sensor technologies, multi-lead electrocardiogram (ECG) monitoring systems can be implemented for real-time Cardiovascular Disease (CVD) tracking and prevention services by using mobile terminals. To meet this objective, we designed a 7-lead ECG monitoring system enabled by smartphones, which is a combination of user mobility requirement and clinical intelligent function. In the system, an application-layer protocol is conceived and tested for guaranteeing data transmission reliability between smartphones and portable sensors. In addition, the smartphone in the system can be customized as a personal health manager, which can control system function modes and device states, and also perform information management and deeper data analysis. Most significantly, we developed a health risk alarm algorithm to detect ECG signal abnormities, which could help professionals pick out the data with key clinical information. To test our system performance and validity, we carried out simulation tests and system experiments. The results show our system is helpful in CVD prevention services.

System light-loading technology for mHealth: Manifold-learning-based medical data cleansing and clinical trials in WE-CARE Project.

Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient teleCARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level.

Asymptotic Performance Analysis of Multihop Relaying with Co-Channel Interference in Nakagami-m Fading Channels

In this letter, the asymptotic performance of channel state information-assisted amplify-and-forward multihop relaying with co-channel interference (CCI) at both relays and destination over Nakagami-m fading channels is investigated. Assuming integer fading parameter m for source → relay → destination links and arbitrary fading parameter m for CCI links, we derive closed-form expressions for bit error rate of the system at high signal-to-interference-plus-noise regime and characterize the diversity order and coding gain achieved by the system. Simulation results are provided to validate the analysis.

Dimensionality Reduction for Anomaly Detection in Electrocardiography: A Manifold Approach

ECG analysis is universal and important in miscellaneous medical applications. However, high computation complexity is a problem which has been shown in several levels of conventional data mining algorithms for ECG analysis. In this paper, we presented a novel manifold approach to visualize and analyze the ECG signal. According to regularity of the data, our algorithm can discover the intrinsic structure and represent the streaming data with a 1-D manifold on a 2-D space. Furthermore, the proposed algorithm can reliably detect the anomaly in ECG streaming data. We evaluated the performance of the algorithm with two different anomalies in wearable applications: for the anomaly from heart disorders such as apnea, arrythmia, our algorithm could achieve up to 90% recognition rate, for the anomaly from the ECG device, our algorithm could detect the outlier with 100%.

A 3R dataflow engine for restoring electrophysiological signals in telemedicine cloud platforms

Today, IT technology plays a key role in promoting health services which provide medical information over a digital information system. Unfortunately, the digitized data may be not understood by medical professionals due to the limitation of conventional medical signal recognition. To deal with this challenge, we design a 3R (Retiming, Regeneration, Reshaping) dataflow engine that can restore electrophysiological data into its original medical patterns. We carried out clinical trials on our cloud platform in PKUs People Hospital. Clinical results proved that our solution produces high playback accuracy and matches with medical diagnosis criteria very well. With the proof of our clinical tests, this solution can be a very useful tool for clinical treatment and diagnosis in medical platforms.

Self-Healing Optical Access Networks (SHOAN) Operated by Optical Switching Technologies

An optical access network should offer low-cost reliable services to its end users. To address this problem, an optimal solution is needed which can turn an optical access architecture into a self-healing system. Hence, we propose the Self-Healing Optical Access Network (SHOAN), in which two or more optical access architectures are partners of each other, and they are interconnected by elementary optical crossbar switches into a simple mesh network. In SHOAN, the crossbar switches can keep each access architecture as an independent and closed system for only serving its own end users in normal state. But the crossbars become open in fault scenarios. Whenever a failure occurs in the network, the fault can be monitored and affected services can be recovered by the partner of the access architecture that is affected. Such an interconnected optical access network can withstand failures in its transmission paths, and recover network services in a self-healing way. Compared to existing solutions (e.g., dual-home architecture), illustrative examples demonstrate that SHOAN has many desirable properties: (1) it is robust because risks are disjointed, (2) it is reliable because service recovery is given top priority, and (3) it has low cost because redundant backup components are not necessary since the partners resources act as backup resources. Analysis results show that SHOAN can minimize disruption duration and network cost for broadband access services.

Fall perception for elderly care: A fall detection algorithm in Smart Wristlet mHealth system

Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at todays socio-economic level.

A novel multi-resolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project

Cardiovascular disease (CVD) has become the leading cause of human deaths today. In order to combat this disease, many professionals are using mobile electrocardiogram (ECG) remote monitoring system. While using mobile ECG systems, most of the cardiac anomalies can be observed, especially when serious myocardial ischemia, heart failure, and malignant arrhythmia occur. Thus, ECG anomaly detection and analysis have attracted more and more attention in the clinical and research communities. Currently, the existing solutions of ECG automatic detection and analysis technologies are challenged by an accuracy requirement. Based on this motivation, we propose a novel Multi-Resolution Support Vector Machine (MR-SVM) algorithm to detect ECG waveform anomaly. This proposal is tested in our WE-CARE (a Wearable Efficient telecardiology system) project. Clinical trials and experimental results show that the algorithm can successfully extract original QRS complex waves and T waves regardless of noise magnitude and distinguish the ST segment morphological anomalies. Compared with European standard ST-T database, our solution can achieve the average T wave recognition accuracy rate of 97.5% and ST anomaly detection accuracy rate of 93%.