Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

EKG-parametrien käyttö kammioperäisten rytmihäiriöiden lyhyen aikavälin ennustamisessa

Malignant spontaneous ventricular arrhythmias, such as ventricular tachycardia (VT) and ventricular fibrillation (VF), are the most common trigger of sudden cardiac death (SCD) in and out of hospital. For a hospitalized patient, occurrence of such arrhythmia is a struggle of life and death where every second of oxygen deprivation, resulting from reduced blood flow, decreases chances of survival. Despite recent advances in resuscitation strategies, survival rates in in-hospital cardiac arrests remain unacceptably low. Main factors contributing to the poor prognosis are lack of patient monitoring and delay in the initiation of resuscitation. Thus, in order to increase the likelihood of successful resuscitation, or prevent the arrhythmia from happening in the first place, continuous and quantitative risk of arrhythmia assessment is required. Currently, however, cardiac monitoring is utilized to detect the onset of life threatening cardiac episodes only. Thus, development of risk indices and the study of precursors of lethal arrhythmias have great clinical value and will lead to better cardiac monitoring. In this thesis, changes in ECG signal preceding lethal cardiac arrhythmias are studied both in different patient groups and in individual patients. Furthermore, an algorithm predicting imminent ventricular tachyarrhythmias is presented. Current knowledge of underlying mechanisms of onset of ventricular arrhythmias is used to assess the risk of arrhythmia continuously during cardiac monitoring of a patient. Our approach is novel and similar assessment of such algorithm has not been published previously. A review of existing methods and applications for risk assessment of SCD with discussion of future trends and possibilities is also given.Malignit kammioperäiset rytmihäiriöt, kuten kammiotakykardia ja kammiovärinä, ovat yleisimpiä syitä sydänperäiseen äkkikuolemaan sekä sairaalassa että sen ulkopuolella. Sairaalassa kuten sen ulkopuolellakin tällaiset rytmihäiriöt ovat aina hengenvaarallisia ja pitkittyessään vähentynyt tai pysähtynyt hapenkuljetus elimistöön pienentää todennäköisyyttä selviytyä. Huolimatta viimeaikaisista ponnisteluista viiveettömän ja tehokkaamman elvytyksen eteen, sairaalassa tapahtuvien sydänkohtausten ennuste on pysynyt huonona. Tämä johtuu lähinnä viiveestä elvytyksen aloittamisessa ja monitoroinnin puutteesta, joten oleellisinta ennusteen parantamisen kannalta olisi jatkuva rytmihäiriöriskin kvantitatiivinen arviointi potilasmonitoroinnilla. Näin useat rytmihäiriöt voitaisiin estää ja alkaviin voitaisiin reagoida nopeammin. Nykyisin potilasmonitorointi on kuitenkin keskittynyt jo alkaneiden rytmihäiriöiden tunnistamiseen eikä ennustavia ratkaisuja ole tarjolla. äkillistä sydänkohtausta edeltävien ilmiöiden tutkiminen ja rytmihäiriöriskin määrittäminen kajoamattomalla potilasmonitoroinnilla ovat ensisijaisen tärkeitä, mikäli rytmihäiriöpotilaiden ennustetta halutaan parantaa sairaalaympäristössä. Tässä opinnäytteessä tutkitaan rytmihäiriöitä edeltäviä muutoksia EKG-signaalista mitattavissa parametreissa eri potilasryhmissä ja yksittäisillä potilailla. Esittelemme algoritmin, joka arvioi EKG:sta mitatuista parametreista yksittäisen potilaan riskiä rytmihäiriön käynnistymiseen. Valitsemamme lähestymistapa poikkeaa täysin olemassa olevista eikä vastaavia tuloksia ole aikaisemmin julkaistu. Algoritmin kehityksessä hyödynnetään laajasti olemassa olevaa tutkimustietoa rytmihäiriöiden käynnistymisestä ja ylläpidosta. Olemassa olevat menetelmät on esitelty laajassa kirjallisuuskatsauksessa. Opinnäytetyön lopussa algoritmin kliinistä hyödyllisyyttä ja tulevia kehitysnäkymiä on arvioitu saavutettujen tulosten valossa

Cardiovascular data analytics for real time patient monitoring

Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.Doctor of Philosoph

Role of independent component analysis in intelligent ECG signal processing

The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very tedious task thus yielding limited descriptions. In addition, a manual ECG analysis is always prone to errors: human oversights. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification.Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. This thesis focuses on ECG signal processing using Independent Component Analysis (ICA), which has received increasing attention as a signal conditioning and feature extraction technique for biomedical application. Long term ECG monitoring is often required to reliably identify the arrhythmia. Motion induced artefacts are particularly common in ambulatory and Holter recordings, which are difficult to remove with conventional filters due to their similarity to the shape of ectopic xiiibeats. Feature selection has always been an important step towards more accurate, reliable and speedy pattern recognition. Better feature spaces are also sought after in ECG pattern recognition applications. Two new algorithms are proposed, developed and validated in this thesis, one for removing non-trivial noises in ECGs using the ICA and the other deploys the ICA extracted features to improve recognition of arrhythmias. Firstly, independent component analysis has been studiedand found effective in this PhD project to separate out motion induced artefacts in ECGs, the independent component corresponding to noise is then removed from the ECG according to kurtosis and correlation measurement.The second algorithm has been developed for ECG feature extraction, in which the independent component analysis has been used to obtain a set of features, or basis functions of the ECG signals generated hypothetically by different parts of the heart during the normal and arrhythmic cardiac cycle. ECGs are then classified based on the basis functions along with other time domain features. The selection of the appropriate feature set for classifier has been found important for better performance and quicker response. Artificial neural networks based pattern recognition engines are used to perform final classification to measure the performance of ICA extracted features and effectiveness of the ICA based artefacts reduction algorithm.The motion artefacts are effectively removed from the ECG signal which is shown by beat detection on noisy and cleaned ECG signals after ICA processing. Using the ICA extracted feature sets classification of ECG arrhythmia into eight classes with fewer independent components and very high classification accuracy is achieved

Time series analysis and modeling of the statistical properties of heart beat dynamics during atrial fibrillation

The present work makes a contribution to the statistical analysis of medical time series. In particular, the work discusses the possibility of detecting the rate of fibrillation – observed in the atrium during atrial fibrillation - based solely on the statistical properties of ventricular interbeat-intervals. First the work concentrates on the statistical analysis of ECG-recordings and of ventricular tachograms. It presents a new method – the generation of so-called morphograms – which enables one to analyse the entire information carried by an ECG-recording without complicated pattern recognition. It is shown that this method is suitable for determining different states of health. Second the work concentrates on the analysis of the statistical properties of ventricular interbeat-intervals, observed during atrial fibrillation. It presents a new characteristic, statistical feature – an exponential decay in the distribution of those intervals - which has not been published before. Furthermore it is shown, that different statistical features of ventricular interbeat-intervals during atrial fibrillation are interrelated. In the following the work concentrates on the mathematical modelling of the physiological process during the conduction of atrial impulses through the AV node. In particular the work concentrates on the conduction model originally proposed by Jorgensen et al.. It is shown that this model can be fully analytical described. Based on a detailed discussion of the original model and mathematical solutions for modified ones, two methods for determining the fibrillation rate are presented. The performance of these methods are demonstrated based on medical data. The present work demonstrates that during atrial fibrillation the rate of fibrillation can be determined solely based on the statistical properties of ventricular interbeat-intervals

Artificial Intelligence for Data Analysis and Signal Processing

Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields

ADAPTIVE MODELS-BASED CARDIAC SIGNALS ANALYSIS AND FEATURE EXTRACTION

Signal modeling and feature extraction are among the most crucial and important steps for stochastic signal processing. In this thesis, a general framework that employs adaptive model-based recursive Bayesian state estimation for signal processing and feature extraction is described. As a case study, the proposed framework is studied for the problem of cardiac signal analysis. The main objective is to improve the signal processing aspects of cardiac signals by developing new techniques based on adaptive modelling of electrocardiogram (ECG) wave-forms. Specially several novel and improved approaches to model-based ECG decomposition, waveform characterization and feature extraction are proposed and studied in detail. In the concept of ECG decomposition and wave-forms characterization, the main idea is to extend and improve the signal dynamical models (i.e. reducing the non-linearity of the state model with respect to previous solutions) while combining with Kalman smoother to increase the accuracy of the model in order to split the ECG signal into its waveform components, as it is proved that Kalman filter/smoother is an optimal estimator in minimum mean square error (MMSE) for linear dynamical systems. The framework is used for many real applications, such as: ECG components extraction, ST segment analysis (estimation of a possible marker of ventricular repolarization known as T/QRS ratio) and T-wave Alternans (TWA) detection, and its extension to many other applications is straightforward. Based on the proposed framework, a novel model to characterization of Atrial Fibrillation (AF) is presented which is more effective when compared with other methods proposed with the same aims. In this model, ventricular activity (VA) is represented by a sum of Gaussian kernels, while a sinusoidal model is employed for atrial activity (AA). This new model is able to track AA, VA and fibrillatory frequency simultaneously against other methods which try to analyze the atrial fibrillatory waves (f-waves) after VA cancellation. Furthermore we study a new ECG processing method for assessing the spatial dispersion of ventricular repolarization (SHVR) using V-index and a novel algorithm to estimate the index is presented, leading to more accurate estimates. The proposed algorithm was used to study the diagnostic and prognostic value of the V-index in patients with symptoms suggestive of Acute Myocardial Infraction (AMI)

An investigation into the effects of commencing haemodialysis in the critically ill

<b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature

From Ecological Epitome to Medical Model: An investigation into Applications for the use of Daphnia in Heart Science.

The primary aim of this research was to determine whether Daphnia might become a model for cardiovascular concentration-response trials. This would provide a high throughput means of testing cardiac therapeutics without resort to small mammal trials. We found Daphnia are inappropriate in this context due to high population variance and sensitivity to small, subtle, environmental changes. A new aim was developed to determine whether beat-to-beat variation could be correlated with an individual’s response to toxic insult. Further, to develop more accurate and efficient means of gathering heart rhythm data by recording heart movement from whole live Daphnia. This opens the way to individualising cardio therapeutics; by correlating the stability of individual hearts with response to cardiac insult, regression analysis provides a means of finding a prediction tool. Daphnia are a convenient example here, but successful scoring systems might also be applied to the human heart via analysis of ECG readouts. Collecting signals from whole live Daphnia did not fulfil the goal of gathering heart data as this instead recorded limb movement. However, this provides a means of improving toxicology testing in aquatic ecology. This thesis offers three contributions to knowledge: 1. Daphnia are an inappropriate model for cardiovascular therapeutic dose-response trials due to extreme environmental sensitivities. 2. Baseline heart rhythm can be correlated with paired response to cardiac insult, with significance at the 0.01 alpha level, using an adjusted version of the Lyapnov equation; Finite Time Growth (Wessel, 2010). However, this is only if population variation is adequate. It is better applied to a natural in situ population than a homegenic lab population. 3. A novel technique for measuring Daphnia electromechanical movement records feeding limbs rather than the heart. This offers a novel and more efficient technique for aquatic ecotoxicology, where visual observation or films of the same are currently used