A Novel Application for Real-time Arrhythmia Detection using YOLOv8

In recent years, there has been an increasing need to reduce healthcare costs in remote monitoring of cardiovascular health. Detecting and classifying cardiac arrhythmia is critical to diagnosing patients with cardiac abnormalities. This paper shows that complex systems such as electrocardiograms (ECG) can be applicable for at-home monitoring. This paper proposes a novel application for arrhythmia detection using the state-of-the-art You-Only-Look-Once (YOLO)v8 algorithm to classify single-lead ECG signals. We proposed a loss-modified YOLOv8 model that was fine-tuned on the MIT-BIH arrhythmia dataset to detect to allow real-time continuous monitoring. Results show that our model can detect arrhythmia with an average accuracy of 99.5% and 0.992 mAP@50 with a detection time of 0.002s on an NVIDIA Tesla V100. Our study demonstrated the potential of real-time arrhythmia detection, where the model output can be visually interpreted for at-home users. Furthermore, this study could be extended into a real-time XAI model, deployed in the healthcare industry, and significantly advancing healthcare needs

Intelligent adaptive monitoring for cardiac surveillance

International audienceMonitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among alarms those that necessitate urgent therapeutic action. We propose an intelligent monitoring system that makes use of many artificial intelligence techniques: artificial neural networks for temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Apprentissage d'arbre de décision pour le pilotage en ligne d'algorithmes de détection sur les électrocardiogrammes

National audienceLe nombre d'algorithmes de traitement du signal (compression, reconnaissance des formes, etc.) grandit progressivement ce qui rend de plus en plus difficile le choix de l'algorithme le plus adapté à une tâche particulière. Ceci est particulièrement vrai pour l'analyse automatique des électrocardiogrammes (ECG) notamment pour la détection des complexes QRS. Bien que chaque algorithme de la littérature se comporte de manière satisfaisante dans des situations normales, il existe des contextes où un algorithme est plus adapté que les autres, notamment en présence de bruit. Nous proposons une méthode de sélection qui choisit, en ligne, l'algorithme le plus adapté au contexte courant du signal à traiter. Les règles de sélection sont acquises par arbre de décision sur les résultats de performance de 7 algorithmes testés dans 130 contextes différents. Les résultats montrent la supériorité de l'approche proposée sur les algorithmes utilisés séparément. En outre, les performances des règles de sélection apprises sont très proches de celles des règles acquises par expertise, ce qui conforte notre approche

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)

Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize the spread of the virus. The need for providing care to patients at home is essential. Internet of Things (IoT) is widely known and used by different fields. IoT based homecare will help in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as minimizing human exertions, economical savings and improved efficiency and effectiveness. One of the important requirement on homecare system is the accuracy because those systems are dealing with human health which is sensitive and need high amount of accuracy. Moreover, those systems deal with huge amount of data due to the continues sensing that need to be processed well to provide fast response regarding the diagnosis with minimum cost requirements. Heart is one of the most important organ in the human body that requires high level of caring. Monitoring heart status can diagnose disease from the early stage and find the best medication plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram (ECG) signals are used to track heart condition using waves and peaks. Accurate and efficient IoT ECG monitoring at home can detect heart diseases and save human lives. As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used; one online which is old and limited, and another huge, unique and special from real patients in hospital. The raw ECG signal for each patient is passed through the implemented Low Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any external interference. The clear signal in this model is passed through feature extraction stage to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to apply classification on them. For the diagnosis purpose a classification stage is made using three classification ways; threshold values, machine learning and deep learning to increase the accuracy. Threshold values classification technique worked based on medical values and boarder lines. In case any feature goes above or beyond these ranges, a warning message appeared with expected heart disease. The second type of classification is by using machine learning to minimize the human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm on the features extracted from both databases. The classification accuracy for online and hospital databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP) Neural Network is implemented to improve the accuracy and reduce the errors. The number of errors reduced to 0.019 and 0.006 using online and hospital databases. While using hospital database which is huge, there is a need for a technique to reduce the amount of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed. This algorithm is able to make diagnosis of heart disease from the reduced size using compressed ECG signals with high level of accuracy and low cost. The extracted features from compressed and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine learning and deep learning classification accuracy without the need for any reconstructions. The throughput is improved by 43% with reduced storage space of 57% when using data compression. Moreover, to achieve fast response, the amount of data should be reduced further to provide fast data transmission. A compressive sensing based cardiac homecare system is presented. It gives the channel between sender and receiver the ability to carry small amount of data. Experiment results reveal that the proposed models are more accurate in the classification of Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast diagnosis and minimum cost requirements. Based on the experiments on classification accuracy, number of errors and false alarms, the dictionary of the compressive sensing selected to be 900. As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy in addition to minimizing data and cost requirements

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

An Algorithm to detect atrial fibrillation using short ECG segments

La fibrilación atrial es una enfermedad difícil de detectar hasta que se manifiesta de forma seria. Sin embargo, si se detecta con suficiente tiempo se puede aplicar un tratamiento para que sus síntomas estén controlados y no llegue a ser mortal. El objetivo de este proyecto es desarrollar un software que permita la detección de fibrilaciones atriales en electrocardiogramas de corta duración. Esto posibilitaría una temprana detección en electrocardiogramas realizados en centros de salud y hospitales, sin tener que hacer un estudio largo de la actividad cardiaca. Para llevar a cabo este trabajo se trabajara con el concepto de Deep Learning. Antes de ello, se implementa un detector de latidos automático, el cual debe ser muy preciso en la detección del pico de la onda R en el complejo QRS. A continuación se crea una red neuronal que será la encargada de diferenciar entre fibrilaciones atriales y ritmos sinusales. Por último, se evalúan los resultados obtenidos y se plantean los diferentes beneficios que supondría su implementación.Fibrilazio aurikularra gaixotasun zaila da hautemateko, modu larrian agertzen den arte. Hala ere, denbora nahikorekin hautematen bada, sintomak kontrolatzeko tratamendua jar daiteke eta ez da hilgarri izango. Proiektu honen helburua epe laburreko elektrokardiogrametan fibrilazio aurikularrak detektatzeko softwarea garatzea da. Modu honetan osasun zentroetan eta ospitaleetan burutu diren elektrokardiogrametan fibrilazio aurikularrak goiz detektatzea ahalbidetuko litzateke, bihotz-jardueraren azterketa luzea egin beharik gabe. Lan hau burutzeko Deep Learning kontzeptua lantzen dugu. Honen aurretik, bihotz taupaden detektore automatiko bat jarri da. Detektorea, oso zehatza izan behar du QRS konplexuko R uhinaren gailurra hautemateko. Ondoren, fibrilazio atrialen eta erritmo normalen artean bereizteko ardura duen neurona-sarea sortuko da. Azkenik, lortutako emaitzak ebaluatzen dira eta metodoaren aplikazioak dituen onura ezberdinak aztertuko dira.Atrial fibrillation is a difficult disease to detect until it manifests in a serious way. However, if it is detected early enough, treatment can be applied so that symptoms are controlled and do not become deadly. The objective of this project is to develop software that allows the detection of atrial fibrillations in short-term electrocardiograms. This would allow early detection in electrocardiograms performed in health centers and hospitals, without having to make a long study of cardiac activity. To carry out this work we will work with the concept of Deep Learning. Before that, an automatic heartbeat detector is implemented, which must be very accurate in detecting the peak of the R wave in the QRS complex. Then, a neural network is created that will be responsible for differentiating between atrial fibrillations and sinus rhythms. Finally, the results are evaluated and considered the various benefits that will have its implementation.

An Algorithm to detect atrial fibrillation using short ECG segments

La fibrilación atrial es una enfermedad difícil de detectar hasta que se manifiesta de forma seria. Sin embargo, si se detecta con suficiente tiempo se puede aplicar un tratamiento para que sus síntomas estén controlados y no llegue a ser mortal. El objetivo de este proyecto es desarrollar un software que permita la detección de fibrilaciones atriales en electrocardiogramas de corta duración. Esto posibilitaría una temprana detección en electrocardiogramas realizados en centros de salud y hospitales, sin tener que hacer un estudio largo de la actividad cardiaca. Para llevar a cabo este trabajo se trabajara con el concepto de Deep Learning. Antes de ello, se implementa un detector de latidos automático, el cual debe ser muy preciso en la detección del pico de la onda R en el complejo QRS. A continuación se crea una red neuronal que será la encargada de diferenciar entre fibrilaciones atriales y ritmos sinusales. Por último, se evalúan los resultados obtenidos y se plantean los diferentes beneficios que supondría su implementación.Fibrilazio aurikularra gaixotasun zaila da hautemateko, modu larrian agertzen den arte. Hala ere, denbora nahikorekin hautematen bada, sintomak kontrolatzeko tratamendua jar daiteke eta ez da hilgarri izango. Proiektu honen helburua epe laburreko elektrokardiogrametan fibrilazio aurikularrak detektatzeko softwarea garatzea da. Modu honetan osasun zentroetan eta ospitaleetan burutu diren elektrokardiogrametan fibrilazio aurikularrak goiz detektatzea ahalbidetuko litzateke, bihotz-jardueraren azterketa luzea egin beharik gabe. Lan hau burutzeko Deep Learning kontzeptua lantzen dugu. Honen aurretik, bihotz taupaden detektore automatiko bat jarri da. Detektorea, oso zehatza izan behar du QRS konplexuko R uhinaren gailurra hautemateko. Ondoren, fibrilazio atrialen eta erritmo normalen artean bereizteko ardura duen neurona-sarea sortuko da. Azkenik, lortutako emaitzak ebaluatzen dira eta metodoaren aplikazioak dituen onura ezberdinak aztertuko dira.Atrial fibrillation is a difficult disease to detect until it manifests in a serious way. However, if it is detected early enough, treatment can be applied so that symptoms are controlled and do not become deadly. The objective of this project is to develop software that allows the detection of atrial fibrillations in short-term electrocardiograms. This would allow early detection in electrocardiograms performed in health centers and hospitals, without having to make a long study of cardiac activity. To carry out this work we will work with the concept of Deep Learning. Before that, an automatic heartbeat detector is implemented, which must be very accurate in detecting the peak of the R wave in the QRS complex. Then, a neural network is created that will be responsible for differentiating between atrial fibrillations and sinus rhythms. Finally, the results are evaluated and considered the various benefits that will have its implementation.