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.

A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

Wearable Wireless Devices

No abstract available

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Feature Selection and Non-Euclidean Dimensionality Reduction: Application to Electrocardiology.

Heart disease has been the leading cause of human death for decades. To improve treatment of heart disease, algorithms to perform reliable computer diagnosis using electrocardiogram (ECG) data have become an area of active research. This thesis utilizes well-established methods from cluster analysis, classification, and localization to cluster and classify ECG data, and aims to help clinicians diagnose and treat heart diseases. The power of these methods is enhanced by state-of-the-art feature selection and dimensionality reduction. The specific contributions of this thesis are as follows. First, a unique combination of ECG feature selection and mixture model clustering is introduced to classify the sites of origin of ventricular tachycardias. Second, we apply a restricted Boltzmann machine (RBM) to learn sparse representations of ECG signals and to build an enriched classifier from patient data. Third, a novel manifold learning algorithm is introduced, called Quaternion Laplacian Information Maps (QLIM), and is applied to visualize high-dimensional ECG signals. These methods are applied to design of an automated supervised classification algorithm to help a physician identify the origin of ventricular arrhythmias (VA) directed from a patient's ECG data. The algorithm is trained on a large database of ECGs and catheter positions collected during the electrophysiology (EP) pace-mapping procedures. The proposed algorithm is demonstrated to have a correct classification rate of over 80% for the difficult task of classifying VAs having epicardial or endocardial origins.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113303/1/dyjung_1.pd

Conception de système de traitement de données sur les émotions d’un être humain dans un environnement mobile et incertain

Une émotion humaine est considérée comme un état d’esprit d’un individu qui est complexe et intense, débutant de manière brutale et peut durer pendant une période relativement brève. Les émotions affectent généralement à la fois l’état physiologique et psychologique et peuvent aider à améliorer la santé humaine et l’efficacité au travail si elles sont positives, tandis que les émotions négatives peuvent causer des problèmes de santé et de comportements très graves. La détection et la surveillance des émotions sont primordiales dans de nombreux domaines tels que la conduite de véhicules, afin d’agir dans le temps opportun en cas de présence d’un état émotionnel négatif qui peut affecter dangereusement la vie du conducteur. Dans la science, on a défini plusieurs méthodes de détection d’émotions qui peuvent être classifiées en deux grandes catégories ; l’une utilise les signaux physiques humains tels que l’expression faciale, la parole, le geste, la posture, etc., qui ont l’avantage d’être facilement collectés et étudiés, mais qui souffrent d’une fiabilité modeste en raison de la possibilité de ne pas montrer les signaux physiques vrais pour cacher de véritables émotions. La deuxième catégorie utilise les signaux internes (les signaux physiologiques), qui comprennent l’électrocardiogramme (ECG), l’électroencéphalogramme (EEG), la température (T), l’électromyogramme (EMG), etc. qui sont plus fiable due à leur nature interne et non contrôler directement par l’être humain. Dans cette thèse, nous avons étudié le problème de la détection des émotions humaines chez un conducteur de véhicule en se basant sur le signal ECG. Pour cela, nous avons proposé trois contributions liées à la détection des émotions. La première est une approche d’optimisation des paramètres de classification des catégories des signaux ECG qui parmi elle une classe ECG anormale représentant un état émotionnel inhabituel. La deuxième contribution est une version améliorée de l’approche Random Forest pour la dé- tection de l’état du stress d’un conducteur. La troisième contribution est un système de détection d’émotions en suggérant une approche d’apprentissage profond ; il s’agit d’un nouveau réseau de ivneurones convolutif et d’augmentation de données qui considère la variabilité de la fréquence cardiaque (Heart Rate Variability -HRV-) comme critère essentiel de détection. Le système proposé a été bien développé et prouvé par une étude de validation et une comparaison avec les travaux de référence similaires proposés dans la littérature

A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural networkComment: 13 pages, 10 figures, Submitted to Elsevier's Array Journa

Pulmonary vein isolation in treatment of atrial fibrillation using radiofrequency or cryoballoon ablation: factors associated with better clinical outcomes

Background: Electrical pulmonary vein isolation (PVI) is still regarded as a cornerstone for treatment of paroxysmal and persistent atrial fibrillation (AF). It can be achieved by different techniques. We investigated the indications and techniques of PVI using radiofrequency ablation (RFA) and cryoballoon ablation (CBA) for AF and compared the efficacy of the two techniques for persistent AF. Methods and results: First, we conducted a prospective, randomized (1:1), open-label, multi-centre clinical trial to evaluate the effectiveness of PVI performed with CBA in comparison with contact force-sensing RFA in patients with persistent AF. A total of 101 patients (52 in CBA and 49 in RFA) were enrolled and followed up for 12 months. The CBA group showed a similar clinical outcome to RFA in terms of freedom from atrial tachyarrhythmia at 12 months (69.2% in CBA vs. 61.2% in RFA, P=0.393). In addition, CBA showed comparable complications (1 in CBA vs. 4 in RFA, P=0.353), less atrial flutter (AFL) recurrence (3.9% in RFA vs. 18.0% in CBA, P=0.020), and shorter procedure and ablation time (158.9±28.9 vs. 197.9±38.4 minutes, 35.8±6.5 vs. 55.9±16.7 minutes, respectively, both P<0.001) than RFA. Second, we conducted an observational study in an RFA population, to investigate the impacts of procedural parameters on durability of PVI. We analysed the impacts of contact force (CF), power, and application time on ablation effect indicated by impedance drop (ID) in an RFA procedure with both conventional and high-power short-duration (HPSD) settings. We found that: (i) The minimum requirement of CF for effective ablation was 5 g. (ii) With CF ≥5 g, CF, power and application time can compensate for each other within restricted ranges, while the time to reach maximal ablation effect can be shortened by increasing CF or power output. (iii) The effect of HPSD ablation with 50 W for 10 s is equivalent to conventional ablation with 25 W for 40 s and 30-35 W for 20-30 s, in terms of ID. Changes of ID with increasing ablation index were similar at 30, 35 and 50 W. At 25 W they showed the same trend, but with smaller ID at the same ablation index. Third, we analysed the predictive value of procedural and biophysical parameters for the durability of PVI in a CBA population in a retrospective case-control study that used the data from 241 pulmonary veins of 71 patients who underwent a repeat AF ablation procedure. Thawing plateau time (TimeTP, defined as the time from 0 to 10℃ inside the balloon in the thawing period) was shown to be the strongest independent predictor for the durability of PVI. The relationship between TimeTP and the durability of PVI presents in a dose-proportional manner. TimeTP 25 s predicts durable PVI. In these two studies, we provided practical data for optimizing dose strategies for RFA and CBA to improve the durability of PVI. Finally, we performed a retrospective cohort study to investigate the incidence and risk factors for AF in 117 patients who suffered mostly AFL and underwent an elective cavotricuspid isthmus (CTI) ablation. During a mean follow-up period of 68 ± 24 months, 89 patients (70%) developed AF, 53 patients (42%) underwent AF ablation procedures, and 10 patients (8%) developed non-fatal ischemic cerebral events. Independent predictors for additional AF ablation included a higher CHA2DS2-vasc score (odds ratio (OR) 0.72, 95% confidence interval (CI), 0.53–0.98), documentation of both pre- and intraprocedural AF (OR 3.81, 95% CI, 1.14–12.8), and previous use of flecainide (OR 2.43, 95% CI, 1.06–5.58). We emphasized the high risk of AF occurrence and PVI in the future for AFL patients. The findings indicate that CTI block has limited prophylactic effect for AF episodes and that prophylactic PVI may be applied in selective AFL patients. Conclusions: (i) Compared with RFA, PVI performed by CBA offers shorter ablation time and procedure duration, with less AFL recurrence and similar freedom from atrial tachyarrhythmias at 12-month follow-up. (ii) Procedural parameters have predictive value and significant impacts on durability of PVI. (iii) Patients undergoing AFL ablation are at high risk of developing AF in the future and prophylactic PVI may be applied in selective AFL patients.Doktorgradsavhandlin