scholarly journals A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

2021 ◽  
Vol 11 (15) ◽  
pp. 6711
Author(s):  
Manuel M. Casas ◽  
Roberto L. Avitia ◽  
Jose Antonio Cardenas-Haro ◽  
Jugal Kalita ◽  
Francisco J. Torres-Reyes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Scientific Community ◽  
Continuous Monitoring ◽  
Automatic Detection ◽  
Morphological Features ◽  
Computational Method ◽  
Historic Data ◽  
General Classifier ◽  
Ecg Data

Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.

scholarly journals DETECÇÃO EM TEMPO REAL DA FREQUÊNCIA CARDÍACA DE PESSOAS POR MEIO DA ANÁLISE DE VARIAÇÕES TEMPORAIS EM VÍDEOS

e-xacta ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 49 ◽  
Author(s):  
Kessiler Almeida Silveira Rodrigues ◽  
Moisés Henrique Ramos Pereira ◽  
Flávio Luis Cardeal Pádua
Keyword(s):  
Heart Rate ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Computational Method ◽  
Burn Victims ◽  
Non Invasive ◽  
Statistical Confidence ◽  
Common Causes ◽  
Supportive Environments

<p>As doenças cardiovasculares são, atualmente, as causas mais comuns de morbimortalidade no mundo. Na perspectiva da prevenção de doenças e agravos, tornam-se fundamentais ações que criem ambientes favoráveis à saúde e favoreçam escolhas saudáveis. Medidas de prevenção e monitoramento contínuo de sinais vitais são necessários, sendo a frequência cardíaca um sinal promissor. No entanto, tal monitoramento pode ser difícil e pouco eficiente, quando não impossível, em determinados casos, como por exemplo, vítimas de queimaduras. Este artigo propõe uma aplicação para monitoramento da frequência cardíaca não invasivo e sem a necessidade de contato, podendo ser manuseado por qualquer pessoa. Para a determinação da frequência cardíaca, a aplicação combina técnicas de processamento de imagens, tratamento de sinais fotopletismográficos e análise de variações temporais em vídeos. Os resultados obtidos demonstram que, considerando 95% de confiança estatística e um erro padrão de 1,08 batimentos por minuto, a aplicação desenvolvida possui a mesma média para aferições de batimentos cardíacos em relação a um dispositivo já consolidado no mercado para essa finalidade, mostrando-se como um método computacional promissor para medições em repouso.</p><p>Abstract </p><p>Cardiovascular diseases are currently the most common causes of morbidity and mortality worldwide. From the perspective of prevention of diseases and disorders, become fundamental actions that create supportive environments for health and promote healthy choices. Prevention and continuous monitoring of vital signs are necessary, and the heart rate a promising sign. However, such monitoring can be difficult and inefficient, if not impossible, in certain cases, such as burn victims. This paper proposes an application for monitoring heart rate non-invasive and without the need to touch and can be handled by anyone. For the determination of heart rate the application combines techniques of image processing, processing and analysis of signals photo-plethysmography temporal changes in video. The obtained results show that, considering a 95% statistical confidence and a standard error of 1.08 beats per minute, the developed application has the same average heartbeats' measurements in relation to a consolidated device on the market used for the same purpose, showing itself as a promising computational method for rest measurements.</p>

Nocturnal headache: systemic arterial pressure and heart rate during sleep

Cephalalgia ◽  
1983 ◽  
Vol 3 (1_suppl) ◽  
pp. 54-57 ◽  
Author(s):  
Fabio Cirignotta ◽  
Giorgio Coccagna ◽  
Tommaso Sacquegna ◽  
Emiliana Sforza ◽  
Giuseppe Lamontanara ◽  
...  
Keyword(s):  
Heart Rate ◽  
Arterial Pressure ◽  
Rem Sleep ◽  
Continuous Monitoring ◽  
Systemic Arterial Pressure ◽  
Fréquence Cardiaque ◽  
The Awakening ◽  
Sono Stati ◽  
Autonomic Instability ◽  
Polygraphic Recording

In order to evaluate autonomic nervous system changes occurring before nocturnal headache attacks, we studied three subjects (one male, two females) suffering from chronic migraine. All three patients underwent a nocturnal polygraphic recording including continuous monitoring of systemic arterial pressure and heart rate. Two subjects showed increases and irregularities of arterial pressure before awakening with headache. These changes began during N–REM sleep and lasted during REM sleep preceding the awakening with headache. Heart rate did not change before the attacks. These findings do not support the hypothesis that autonomic instability during REM sleep represents the precipitating factor of the attacks. On a étudié avec des méthodes polygrafiques trois sujets (1 homme et deux femmes) souffrant d'hémicranie chronique avec des crises nocturnes. Chez deux malades les crises étaient précédées d'augmentation et d'irrégularité de la tension artérielle. Ces modifications commençaient pendant le sommeil N-REM et contineaient pendant le sommeil REM qui précédait le réveil avec hémicranie. La fréquence cardiaque n'a pas subi de modification avant les crises. Les résultats obtenus ne confirment l'hypothèse selon laquelle le facteur causant les crises est l'instabilité anticronique à la fase REM. Sono stati studiati con metodiche poligrafiche 3 soggetti (1 maschio e 2 femmine) affetti da emicrania cronica con attacchi notturni. In 2 di essi gli attacchi erano preceduti da incrementi ed irregolarità della pressione arteriosa. Tali modificazioni iniziavano durante il sonno N-REM e perduravano nel corso del sonno REM che precedeva il risveglio con cefalea. La frequenza cardiaca non si modificava prima dell'attacco. I risultati ottenuti non confermano l'ipotesi che il fattore precipitante gli attacchi emicranici sia l'instabilità anticronica della fase REM.

scholarly journals Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

2017 ◽  
Vol 8 ◽  
Author(s):  
Óscar Barquero-Pérez ◽  
Ricardo Santiago-Mozos ◽  
José M. Lillo-Castellano ◽  
Beatriz García-Viruete ◽  
Rebeca Goya-Esteban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Automatic Detection ◽  
Perinatal Hypoxia ◽  
Rate Analysis ◽  
Normalized Compression Distance ◽  
Heart Rate Analysis

High Accuracy of Automatic Detection of Atrial Fibrillation Using Wavelet Transform of Heart Rate Intervals

2002 ◽  
Vol 25 (4) ◽  
pp. 457-462 ◽  
Author(s):  
DAVID DUVERNEY ◽  
JEAN-MICHEL GASPOZ ◽  
VINCENT PICHOT ◽  
FREDERIC ROCHE ◽  
RICHARD BRION ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Wavelet Transform ◽  
Automatic Detection ◽  
High Accuracy

Automatic detection and quantification of sleep apnea using heart rate variability

2010 ◽  
Vol 43 (6) ◽  
pp. 535-541 ◽  
Author(s):  
Saeed Babaeizadeh ◽  
David P. White ◽  
Stephen D. Pittman ◽  
Sophia H. Zhou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Apnea ◽  
Automatic Detection ◽  
Detection And Quantification

scholarly journals Extraction of Fetal Heart Rate from Maternal ECG—Non Invasive Approach for Continuous Monitoring during Labor

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1009 ◽  
Author(s):  
Tashreque Mohammed Haq ◽  
Safkat Arefin ◽  
Shamiur Rahman ◽  
Tanzilur Rahman
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Continuous Monitoring ◽  
Good Accuracy ◽  
Fetal Heart ◽  
Signal Separation ◽  
Invasive Approach ◽  
Non Invasive ◽  
Proposed Model ◽  
Fetal Scalp

Here, we propose a signal processing based approach for the extraction of the fetal heart rate (FHR) from Maternal Abdominal ECG (MAECG) in a non-invasive way. Datasets from a Physionet database has been used in this study for evaluating the performance of the proposed model that performs three major tasks; preprocessing of the MAECG signal, separation of Fetal QRS complexes from that of maternal and estimation of Fetal R peak positions. The MAECG signal is first preprocessed with improved multistep filtering techniques to detect the Maternal QRS (MQRS) complexes, which are dominant in the MAECG. A reference template is then reconstructed based on MQRS locations and removed from the preprocessed signal resulting in the raw FECG. This extracted FECG is further corrected and enhanced before obtaining the Fetal R peaks. The detection of FQRS and calculation of FHR has been compared against the reference Fetal Scalp ECG. Results indicate that the approach achieved good accuracy.

A COMBINED PCA–ICA STATISTICAL APPROACH AND QUADRATIC SPLINE WAVELETS FOR DETECTION OF R-PEAKS AND HEART RATE ESTIMATIONS IN ELECTROCARDIOGRAMS

2011 ◽  
Vol 11 (03) ◽  
pp. 625-642 ◽  
Author(s):  
MANENDRAPAL SINGH CHAWLA
Keyword(s):  
Heart Rate ◽  
Statistical Approach ◽  
Principal Component ◽  
Quadratic Spline ◽  
Redundant Data ◽  
Correlated Channels ◽  
Spline Wavelets ◽  
Threshold Adjustment ◽  
Ica Algorithm ◽  
Ecg Data

The need for the possible improvements in the proposed algorithm is felt toward more effective filtering in the principal component analysis (PCA) preprocessing stage itself, as well for better variance threshold adjustment. Using composite wavelet transform (WT)-based PCA–ICA methods helps for redundant data reduction as well for better feature extraction. This article discusses some of the conditions of ICA that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. In this analysis, a new statistical algorithm is proposed, based on the use of combined PCA–ICA for the three correlated channels of 12-channel electrocardiographic (ECG) data. This study also deals with the detection of QRS complexes in electrocardiograms using combined PCA–ICA algorithm. The efficacy of the combined PCA–ICA algorithm lies in the fact that the location of the R-peaks is accurately determined, and none of the peaks are ignored or missed, as quadratic spline wavelet is also used. With (WT)-based methods, PCA and ICA are used not only for preprocessing, but may also be used for postprocessing based on the requirements, whether ICA is used first then PCA or vice versa.

scholarly journals Clinical Applications of Heart Rate Variability in the Triage and Assessment of Traumatically Injured Patients

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Mark L. Ryan ◽  
Chad M. Thorson ◽  
Christian A. Otero ◽  
Thai Vu ◽  
Kenneth G. Proctor
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Continuous Monitoring ◽  
Trauma Patients ◽  
Clinical Implementation ◽  
Patient Assessment ◽  
Prehospital Triage ◽  
Key Issues ◽  
Medical Disciplines ◽  
Injured Patients

Heart rate variability (HRV) is a method of physiologic assessment which uses fluctuations in the RR intervals to evaluate modulation of the heart rate by the autonomic nervous system (ANS). Decreased variability has been studied as a marker of increased pathology and a predictor of morbidity and mortality in multiple medical disciplines. HRV is potentially useful in trauma as a tool for prehospital triage, initial patient assessment, and continuous monitoring of critically injured patients. However, several technical limitations and a lack of standardized values have inhibited its clinical implementation in trauma. The purpose of this paper is to describe the three analytical methods (time domain, frequency domain, and entropy) and specific clinical populations that have been evaluated in trauma patients and to identify key issues regarding HRV that must be explored if it is to be widely adopted for the assessment of trauma patients.

Longitudinal Analysis of Continuous Pulse Oximetry as Prognostic Factor in Neonatal Respiratory Distress

2020 ◽  
Author(s):  
Gonzalo Solís-García ◽  
Elena Maderuelo-Rodríguez ◽  
Teresa Perez-Pérez ◽  
Laura Torres-Soblechero ◽  
Ana Gutiérrez-Vélez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Intensive Care ◽  
Respiratory Distress ◽  
Oxygen Saturation ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Epidemiological Data ◽  
Monitoring Data ◽  
Joint Models ◽  
Neonatal Icu

Objective Analysis of longitudinal data can provide neonatologists with tools that can help predict clinical deterioration and improve outcomes. The aim of this study is to analyze continuous monitoring data in newborns, using vital signs to develop predictive models for intensive care admission and time to discharge. Study Design We conducted a retrospective cohort study, including term and preterm newborns with respiratory distress patients admitted to the neonatal ward. Clinical and epidemiological data, as well as mean heart rate and saturation, at every minute for the first 12 hours of admission were collected. Multivariate mixed, survival and joint models were developed. Results A total of 56,377 heart rate and 56,412 oxygen saturation data were analyzed from 80 admitted patients. Of them, 73 were discharged home and 7 required transfer to the intensive care unit (ICU). Longitudinal evolution of heart rate (p < 0.01) and oxygen saturation (p = 0.01) were associated with time to discharge, as well as birth weight (p < 0.01) and type of delivery (p < 0.01). Longitudinal heart rate evolution (p < 0.01) and fraction of inspired oxygen at admission at the ward (p < 0.01) predicted neonatal ICU (NICU) admission. Conclusion Longitudinal evolution of heart rate can help predict time to transfer to intensive care, and both heart rate and oxygen saturation can help predict time to discharge. Analysis of continuous monitoring data in patients admitted to neonatal wards provides useful tools to stratify risks and helps in taking medical decisions. Key Points

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