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/

Liikeartefaktat elektrokardiografiassa

Movement of the patient during electrocardiograph (ECG) recording is a severe source of artifacts. Recent technical developments have enabled ECG recording without continuous supervision by experts. However, ECG recording outside of hospitals is prone to poor quality and movement artifacts. Therefore, it is important to study how and how much ECG recordings are affected by movement. Movement artifacts can hide signal components or mimic them, which causes false negative or false positive detections. Methods to manage movement artifacts include both computational and non-computational approaches. Computational approaches include, for example, adaptive filtering and machine learning methods. Additional variables that correlate with the artifact sources can be utilized in artifact recognition. For example, acceleration, impedance, and pressure signals have been studied as possible movement references. These additional signals are recorded by sensors that are placed on the ECG electrodes or on the patient’s body. In this thesis, the effect of movement artifacts is quantified using a simulation. The simulation makes use of open ECG databases. This study investigates how automated ECG analysis is affected by incremental increase in the movement artifact level. According to the results QRS detection statistics worsen with increased artifact levels. Capturing a movement reference for ECG is studied by experimental research. ECG and inertial measurement unit signals were recorded during different movements in order to analyze the creation of movement artifacts and movement reference signals. According to the results, placement of the movement reference signal sensor has a significant effect on the results. Different movements are captured better by different sensors and affect different ECG leads with different strengths.Potilaan liike sydänsähkökäyrämittauksen (EKG) aikana on merkittävä artefaktien lähde. Viimeaikainen teknologinen kehitys on mahdollistanut EKG-mittauksen ilman asiantuntijoiden jatkuvaa valvontaa. EKG-mittaukset sairaalaolosuhteiden ulkopuolella ovat kuitenkin erityisen alttiita huonolle signaalilaadulle ja liikeartefaktoille. Tämän vuoksi on tärkeää tutkia, miten ja kuinka paljon liike vaikuttaa EKG-mittauksiin. Liikeartefaktat voivat joko peittää tai jäljitellä EKG-signaalin eri osia, aiheuttaen vääriä negatiivisia tai vääriä positiivisia havaintoja. Liikeartefaktojen vaikutusta voidaan vähentää sekä laskennallisten että muiden menetelmien avulla. Laskennallisia menetelmiä ovat esimerkiksi adaptiivinen suodatus ja koneoppimismenetelmät. Artefaktojen lähteen kanssa korreloivia muuttujia mittaamalla voidaan edistää artefaktojen tunnistusta EKG-signaalista. Esimerkiksi kiihtyvyys-, impedanssi- ja painesignaalien käyttöä liikereferensseinä on tutkittu. Kyseisiä referenssisignaaleja voidaan mitata EKG-elektrodeihin tai potilaan kehoon kiinnitettävillä sensoreilla. Liikeartefaktojen vaikutuksen suuruutta tutkitaan tässä työssä simulaation avulla. Simulaatiossa hyödynnetään avoimia EKG-tietokantoja. Tutkimuksessa tarkastellaan sitä, miten vähittäinen liikeartefaktatason kasvu vaikuttaa automaattiseen EKG-analyysiin. Tulosten mukaan QRS-detektioon liittyvät tilastot huononevat artefaktatason kasvaessa. Liikereferenssin luomista tarkastellaan kokeellisen tutkimuksen avulla. EKG- ja inertiamittausyksikkö-signaaleja mitattiin erilaisten liikkeiden aikana, jotta voitaisiin havainnoida liikeartefaktojen ja liikesignaalin syntymistä. Tulosten mukaan liikereferenssiä mittaavan sensorin sijoituspaikalla on merkittävä vaikutus tuloksiin. Tietyt liikkeet saadaan paremmin mitattua eri tavoin sijoitettujen sensorien avulla. Lisäksi liikkeet vaikuttavat eri vahvuuksilla eri EKG-kytkentöihin

Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low Stress States. Attention Test

Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should firstly be able to identify different states based on physiological signals. So, the first aim of this work is to identify the most appropriate features, to detect a subject high performance state. For that, a database of electrocardiographic (ECG) and photopletysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, twenty six features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive that the classical sympathetic markers (normalised power in LF and LF/HF ratio) for identifying this attentional state. State classification accuracy reaches a mean of 89 $\pm$ 2%, a maximum of 93% and a minimum of 85%, in the hundred classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulse width, pulse downward slope and mean pulse rate). These results suggest that attentional states could be identified by PPG

Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidenceThis research was funded by the Ministry of Science, Innovation and Universities of Spain, and the European Regional Development Fund of the European Commission, Grant Nos. RTI2018-095324-B-I00, RTI2018-097122-A-I00, and RTI2018-099646-B-I00S

Quality Control in ECG-based Atrial Fibrillation Screening

This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening