Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances.

Fusion of physiological measures for multimodal biometric systems

Coronary Heart Disease (CHD) is the most common cardiovascular disease which has the highest mortality rate in developing countries. To predict and prevent the risk of CHD in its early stages from remote sites, real time monitoring and analysis of an individual’s health statistics is required. Cloud based cyber-physical systems facilitate the alliance of devices in the physical world i.e. cameras, sensors and Geographical Positioning System devices with cyber world to generate the required information. Then it uses cyber world to analyze and share medical information along with localization data with healthcare service providers. Moreover, with the ability to transmit intensive information anytime and anywhere, this technological revolution has raised the level of effective healthcare deliverance. With these aspects, cloud based cyber-physical localization system is proposed to identify the risk level of CHD using adaptive neuro fuzzy inference system at an early stage. The users who are in the middle or high risk category will be monitored continuously to keep track of their electrocardiogram (ECG) readings. In case of any abnormality in ECG readings, an alert will be immediately sent to the user’s mobile phone as well as to the healthcare service providers or professionals to take immediate or necessary action on time for patient’s wellness. It also provides preventive measures and medication according to the risk category of the user. The experimental results reveal that the proposed system efficiently and effectively classifies the risk of CHD as well as utilizes minimum response time in generation of alerts on the basis of ECG readings.

A Fog Assisted Cyber-Physical Framework for Identifying and Preventing Coronary Heart Disease

An intelligent framework for monitoring dengue fever risk using LDA-ANFIS

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition

This paper intends to look into the advent of a technological systems that recently emerged in telemedicine and healthcare domain. Specifically, the paper proposes a Vital Signs Monitoring System VSMS that can be used to control and monitor vital signs of patients such as blood pressure, body temperature, and heart rate pulse. The main purpose of this proposed system is to keep all patients under 24 hour monitoring and be able to alert the staff in case of any abnormalities. The system is built on distributed control system (DCS) architecture. The paper covers three main areas. First, it proposes the system architecture and its subsystems in detail along with all functions. Second, it discusses data historians, which is how to store and handle the aggregated “Big Data” that resulted from continuous monitoring. Finally, how and why this system is handled and built on cloud-based computing environment.

Vital Signs Monitoring System in Cloud Environment

The main aim of this paper is to provide intensive solution for heart attack through Cyber Physical System (CPS) Cyber Physical System is one of the blooming technologies with wide range of applications Currently the interest towards design and implementation of CPS is highly increasing It is expected that CPS will play a vital role in the progress of upcoming embedded systems In this paper we have proposed a system to provide efficient and quick solution to heart attack patients who are at great risk Advancement in Micro Electro Mechanical Systems (MEMS) technology has created an enormous impact on the current medical health care scenario It has enabled the design of small sized wireless communication systems at low cost A small wearable wireless monitoring system inspects the patient's ECG signals continuously and transmits the signals to decision making and control systems The control system is capable of controlling the actuation system which is fitted to the body of the patients The warning system sends an alert in case of emergency to the caretakers mobile Our proposed system uses low cost, energy efficient and reliable zigbee technology for communication among the physical components and hence the effectiveness of the system is improved This life saving system reduces the mortality rate of heart patients and it act as an effective heart care service to patients

S. Sathya

Papers

Cyber Physical System approach for heart attack detection and control using wireless monitoring and actuation system