Similarity Measurement of Biological Signals Using Dynamic Time Warping Algorithm

The problem of similarity measurement of biological signals is considered on this article. The dynamic time warping algorithm is used as a possible solution. A short overview of this algorithm and its modifications are given. Testing procedure for different modifications of DTW, which are based on artificial test signals, are presented

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Quantification of T-wave Morphological Variability Using Time-warping Methods

The aim of this study is to quantify the variation of the T-wave morphology during a 24-hour electrocardiogram (ECG) recording. Two ECG-derived markers are presented to quantify T-wave morphological variability in the temporal, dw, and amplitude, da, domains. Two additional markers, dNLw and dNLa, that only capture the non-linear component of dw and da are also proposed. The proposed markers are used to quantify T-wave time and amplitude variations in 500 24-hour ECG recordings from chronic heart failure patients. Additionally, two mean warped T-waves, used in the calculation of those markers, are proposed to compensate for the rate dependence of the T-wave morphology. Statistical analysis is used to evaluate the correlation between dw, dNLw, da and dNLa and the maximum intra-subject RR range, ΔRR. Results show that the mean warped T-wave is able to compensate for the morphological differences due to RR dynamics. Moreover, the metrics dw and dNLw are correlated with ΔRR, but da and dNLa are not. The proposed dw and dNLw quantify variations in the temporal domain of the T-wave that are correlated with the RR range and, thus, could possibly reflect the variations of dispersion of repolarization due to changes in heart rate

T Wave Amplitude Correction of QT Interval Variability for Improved Repolarization Lability Measurement

Objectives: The inverse relationship between QT interval variability (QTV) and T wave amplitude potentially confounds QT variability assessment. We quantified the influence of the T wave amplitude on QTV in a comprehensive dataset and devised a correction formula. Methods: Three ECG datasets of healthy subjects were analyzed to model the relationship between T wave amplitude and QTV. To derive a generally valid correction formula, linear regression analysis was used. The proposed correction formula was applied to patients enrolled in the Evaluation of Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation trial (DEFINITE) to assess the prognostic significance of QTV for all-cause mortality in patients with non-ischemic dilated cardiomyopathy. Results: A strong inverse relationship between T wave amplitude and QTV was demonstrated, both in healthy subjects (R2 = 0.68, p < 0.001) and DEFINITE patients (R2 = 0.20, p < 0.001). Applying the T wave amplitude correction to QTV achieved 2.5-times better group discrimination between patients enrolled in the DEFINITE study and healthy subjects. Kaplan-Meier estimator analysis showed that T wave amplitude corrected QTVi is inversely related to survival (p < 0.01) and a significant predictor of all-cause mortality. Conclusion: We have proposed a simple correction formula for improved QTV assessment. Using this correction, predictive value of QTV for all-cause mortality in patients with non-ischemic cardiomyopathy has been demonstrated

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

Data semantic enrichment for complex event processing over IoT Data Streams

This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation

Variability of ventricular repolarization dispersion quantified by time-warping the morphology of the T-Waves

Objective: We propose two electrocardiogram (ECG)-derived markers of T-wave morphological variability in the temporal, d¿, and amplitude, da, domains. Two additional markers, d¿NL and daNL, restricted to measure the nonlinear information present within d¿ and da are also proposed. Methods: We evaluated the accuracy of the proposed markers in capturing T-wave time and amplitude variations in 3 situations: 1) In a simulated set up with presence of additive Laplacian noise, 2) when modifying the spatio-temporal distribution of electrical repolarization with an electro-physiological cardiac model, and 3) in ECG records from healthy subjects undergoing a tilt table test. Results:The metrics d¿, da, d¿NL, and daNL followed T-wave time- and amplitude-induced variations under different levels of noise, were strongly associated with changes in the spatio-temporal dispersion of repolarization, and showed to provide additional information to differences in the heart rate, QT and Tpe intervals, and in the T-wave width and amplitude. Conclusion: The proposed ECG-derived markers robustly quantify T-wave morphological variability, being strongly associated with changes in the dispersion of repolarization. Significance: The proposed ECG-derived markers can help to quantify the variability in the dispersion of ventricular repolarization, showing a great potential to be used as arrhythmic risk predictors in clinical situations

Variability of Ventricular Repolarization Dispersion Quantified by Time-Warping the Morphology of the T-waves

We propose two electrocardiogram (ECG)-derived markers of T-wave morphological variability in the temporal, dw , and amplitude, da , domains. Two additional markers, d(NL)w and d(NL)a , restricted to measure the non-linear information present within dw and da are also proposed. METHODS: We evaluated the accuracy of the proposed markers in capturing T-wave time and amplitude variations in 3 situations: (1) In a simulated set up with presence of additive Laplacian noise, (2) when modifying the spatio-temporal distribution of electrical repolarization with an electro-physiological cardiac model and (3) in ECG records from healthy subjects undergoing a tilt table test. RESULTS: The metrics dw , da , d(NL)w and d(NL)a followed T-wave time and amplitude induced variations under different levels of noise, were strongly associated with changes in the spatiotemporal dispersion of repolarization, and showed to provide additional information to differences in the heart rate, QT and Tpe intervals, and in the T-wave width and amplitude. CONCLUSION: The proposed ECG-derived markers robustly quantify T-wave morphological variability, being strongly associated with changes in the dispersion of repolarization. SIGNIFICANCE: The proposed ECG-derived markers can help to quantify the variability in the dispersion of ventricular repolarization, showing a great potential to be used as arrhythmic risk predictors in clinical situations

Analysis of Ventricular Depolarisation and Repolarisation Using Registration and Machine Learning

Our understanding of cardiac diseases has greatly advanced since the advent of electrocardiography (ECG). With the increasing influx of available data in recent times, significant research efforts have been put forth to automate the study and detection of cardiac conditions. Naturally, the focus has progressed toward studying dynamic changes in ventricular depolarisation and repolarisation across serial recordings - as complex beat-to-beat changes in morphology manifest over time. Manual extraction of diagnostic and prognostic markers is a laborious task. Hence, automated and accurate methods are required to extract markers for the study of ventricular lability and detection of common diseases, such as myocardial ischemia and myocardial infarction. The aim of this thesis is to improve automated marker extraction and detection of diseases for the study of ventricular depolarisation and repolarisation lability in ECG. As such, two novel template adaptation methods capable of capturing complex beat-to-beat morphological changes are proposed for three-dimensional and two-dimensional data, respectively. The proposed three-dimensional template adaptation method provides an inhomogeneous method for transforming template vectorcardiogram (VCG) by exploiting registrationinspired parametrisation and an efficient kernel ridge regression formulation. Analysis across simulated data and clinical myocardial infarction data demonstrates state-of-the-art results. The two-dimensional template adaptation method draws from traditional registrationbased techniques and treats the ECG as a two-dimensional point set problem. Validation against previously employed simulated data and a gold-standard annotated clinical database demonstrate the highest level of performance. Subsequently, frameworks employing the proposed template adaptation techniques are developed for the automated detection of ischemic beats and myocardial infarction. Furthermore, a small study analysing ventricular repolarisation variability (VRV) in non-ischemic cardiomyopathy (CM) is considered, utilising markers of cardiac lability proposed in the development of the three-dimensional template adaptation system. In summary, this thesis highlights the necessity for custom template adaptation methods for the accurate measurement of beat-to-beat variability in cardiac data. Two novel stateof- the-art methods are proposed and extended to study myocardial ischemia, myocardial infarction and non-ischemic CM.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure