Personalized reduced 3-lead system formation methodology for Remote Health Monitoring applications and reconstruction of standard 12-lead system

Remote Health Monitoring (RHM) applications encounter limitations from technological front viz. bandwidth, storage and transmission time and the medical science front i.e. usage of 2-3 lead systems instead of the standard 12-lead (S12) system. Technological limitations constraint the number of leads to 2-3 while cardiologists accustomed with 12-Lead ECG may find these 2-3 lead systems insufficient for diagnosis. Thus, the aforementioned limitations pose self-contradicting challenges for RHM. A personalized reduced 2/3 lead system is required which can offer equivalent information as contained in S12 system, so as to accurately reconstruct S12 system from reduced lead system for diagnosis. In this paper, we propose a personalized reduced 3-lead (R3L) system formation methodology which employs principal component analysis, thereby, reducing redundancy and increasing SNR ratio, hence, making it suitable for wireless transmission. Accurate S12 system is made available using personalized lead reconstruction methodology, thus addressing medical constraints. Mean R2 statistics values obtained for reconstruction of S12 system from the proposed R3L system using PhysioNet's PTB and TWA databases were 95.63% and 96.37% respectively. To substantiate the superior diagnostic quality of reconstructed leads, root mean square error (RMSE) metrics obtained upon comparing the ECG features extracted from the original and reconstructed leads, using our recently proposed Time Domain Morphology and Gradient (TDMG) algorithm, have been analyzed and discussed. The proposed system does not require any extra electrode or modification in placement positions and hence, can readily find application in computerized ECG machines

Biometrics for Emotion Detection (BED): Exploring the combination of Speech and ECG

The paradigm Biometrics for Emotion Detection (BED) is introduced, which enables unobtrusive emotion recognition, taking into account varying environments. It uses the electrocardiogram (ECG) and speech, as a powerful but rarely used combination to unravel people’s emotions. BED was applied in two environments (i.e., office and home-like) in which 40 people watched 6 film scenes. It is shown that both heart rate variability (derived from the ECG) and, when people’s gender is taken into account, the standard deviation of the fundamental frequency of speech indicate people’s experienced emotions. As such, these measures validate each other. Moreover, it is found that people’s environment can indeed of influence experienced emotions. These results indicate that BED might become an important paradigm for unobtrusive emotion detection

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices

Investigating the effects of an on-chip pre-classifier on wireless ECG monitoring

In past years, heart disease has been the leading cause of death in most developed countries. Timely detection of a heart condition is necessary in order to prevent life threatening situations. Even when the problem is not a heart condition, the activity of the heart can supply vital information, which makes its monitoring extremely important. A new approach to patient monitoring was taken recently by introducing wireless sensor networks into medical care. The capability of monitoring multiple patients at once makes such a system ideal for pre-hospital and in-hospital emergency care. The main problems associated with wireless sensor networks are power consumption and scaling. The power consumption is a problem due to the need for increased mobility of such a system, while scaling is of concern because a large number of nodes is desired in order to monitor more patients. This thesis addresses the power and bandwidth problems associated with monitoring patients using wireless networks by introducing another level of signal processing at each node. The goal is to design a digital circuit that would detect any abnormality in the ECG signal and enable the data transmission only if such has occurred. Reducing the amount of data being transmitted reduces the necessary bandwidth for each node and with the introduction of the proposed chip, the power consumption of each node is affected as well

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

A Mobile ECG Monitoring System with Context Collection

An objective of a health process is one where patients can stay healthy with the support of expert medical advice when they need it, at any location and any time. An associated aim would be the development of a system which places increased emphasis on preventative measures as a first point of contact with the patient. This research is a step along the road towards this type of preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject’s activities of daily living will, it is hoped, provide an enriched data set for clinicians and so improve clinical decision making. As a first step towards a mobile cardiac wellness guidelines system, the focus of this work is to develop a system that can receive bio-signals wirelessly, analyzing and storing the bio-signal in a handheld device and can collect context information when there are significant changes in bio-signs. For this purpose the author will use a low cost development environment to program a state of the art wireless prototype on a handheld computer that detects and responds to changes in the heart rate as calculated form the interval between successive heart beats. Although the general approach take in this work could be applied to a wide range of bio-signals, the research will focus on ECG signals. The pieces of the system are, A wireless receiver, data collection and storage module An efficient real time ECG beat detection algorithm A rule based (Event-Condition-Action) interactive system A simple user interface, which can request additional information form the user. A selection of real-time ECG detection algorithms have been investigated and one algorithm was implemented in MATLAB [110] and then in Java [142] for this project. In order to collect ECG signals (and in principle any signals) the generalised data collection architecture has also been developed utilizing Java [142] and Bluetooth [5] technology. This architecture uses an implementation of the abstract factory pattern [91] to ensure that the communication channel can be changed conveniently. Another core part of this project is a “wellness” guideline based on Event-Condition-Action (E-C-A) [68] production rule approach that originated in active databases. The work also focuses on design of a guideline based expert system which an E-C-A based implementation will be fully event driven using the Java programming language. Based on the author’s experience and the literature review, some important issues in mobile healthcare along with the corresponding reasons, consequences and possible solutions will be presented

Reconstructing Electrocardiogram Leads From a Reduced Lead Set Through the Use of Patient-Specific Transforms and Independent Component Analysis

In this exploration into electrocardiogram (ECG) lead reconstruction, two algorithms were developed and tested on a public database and in real-time on patients. These algorithms were based on independent component analysis (ICA). ICA was a promising method due to its implications for spatial independence of lead placement and its adaptive nature to changing orientation of the heart in relation to the electrodes. The first algorithm was used to reconstruct missing precordial leads, which has two key applications. The first is correcting precordial lead measurements in a standard 12-lead configuration. If an irregular signal or high level of noise is detected on a precordial lead, the obfuscated signal can be calculated from other nearby leads. The second is the reduction in the number of precordial leads required for accurate measurement, which opens up the surface of the chest above the heart for diagnostic procedures. Using only two precordial leads, the other four were reconstructed with a high degree of accuracy. This research was presented at the 33rd International Conference of the IEEE Engineering in Medicine and Biology Society in 2011.1 The second algorithm was developed to construct a full 12-lead clinical ECG from either three differential measurements or three standard leads. By utilizing differential measurements, the ECG could be reconstructed using wireless systems, which lack the common ground necessary for the standard measurement method. Using three leads distributed across the expanse of the space of the heart, all twelve leads were successfully reconstructed and compared against state of the art algorithms. This work has been accepted for publication in the IEEE Journal of Biomedical and Health Informatics.2 These algorithms show a proof of concept, one which can be further honed to deal with the issues of sorting independent components and improving the training sequences. This research also revealed the possibility of extracting and monitoring additional physiological information, such as a patient\u27s breathing rate from currently utilized ECG systems