An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r2 > 0.8) with the signal acquired from the chest-belt respiration sensor

The extraction of the new components from electrogastrogram (EGG), using both adaptive filtering and electrocardiographic (ECG) derived respiration signal

Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015–0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed.Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015–0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed

The gastrointestinal electrical mapping suite (GEMS): software for analyzing and visualizing high-resolution (multi-electrode) recordings in spatiotemporal detail

BACKGROUND: Gastrointestinal contractions are controlled by an underlying bioelectrical activity. High-resolution spatiotemporal electrical mapping has become an important advance for investigating gastrointestinal electrical behaviors in health and motility disorders. However, research progress has been constrained by the low efficiency of the data analysis tasks. This work introduces a new efficient software package: GEMS (Gastrointestinal Electrical Mapping Suite), for analyzing and visualizing high-resolution multi-electrode gastrointestinal mapping data in spatiotemporal detail. RESULTS: GEMS incorporates a number of new and previously validated automated analytical and visualization methods into a coherent framework coupled to an intuitive and user-friendly graphical user interface. GEMS is implemented using MATLAB®, which combines sophisticated mathematical operations and GUI compatibility. Recorded slow wave data can be filtered via a range of inbuilt techniques, efficiently analyzed via automated event-detection and cycle clustering algorithms, and high quality isochronal activation maps, velocity field maps, amplitude maps, frequency (time interval) maps and data animations can be rapidly generated. Normal and dysrhythmic activities can be analyzed, including initiation and conduction abnormalities. The software is distributed free to academics via a community user website and forum (http://sites.google.com/site/gimappingsuite). CONCLUSIONS: This software allows for the rapid analysis and generation of critical results from gastrointestinal high-resolution electrical mapping data, including quantitative analysis and graphical outputs for qualitative analysis. The software is designed to be used by non-experts in data and signal processing, and is intended to be used by clinical researchers as well as physiologists and bioengineers. The use and distribution of this software package will greatly accelerate efforts to improve the understanding of the causes and clinical consequences of gastrointestinal electrical disorders, through high-resolution electrical mapping

A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems

Detección precoz de la cardiopatía isquémica

La cardiopatía isquémica es una de las principales causas de muerte en los países desarrollados, por lo que su prevención, diagnóstico y tratamiento precoz se ha convertido en un objetivo de vital importancia para disminuir su morbi-mortalidad. En esta revisión se mencionan los principales avances conseguidos para mejorar el diagnóstico precoz de esta patología. A nivel electrocardiográfico, además de los signos ya conocidos como desviaciones del segmento ST, alteraciones en la onda T, aumento duración segmento QRS y cambio del eje eléctrico cardíaco, se han desarrollado diversos parámetros como el cálculo del área desviada del segmento ST, un nuevo algoritmo a partir de potenciales eléctricos tardíos, un nuevo biomarcador eléctrico cardíaco, el cálculo del área de la onda Q y la localización del territorio y vasos afectos según criterios electrocardiográficos. Sin embargo el avance más importante es la aplicación de la tecnología QRS-AF, que con un 75% ± 6% de sensibilidad y un 80% ± 6% de especificidad supera a los anteriores test diagnósticos. En los últimos años ha cobrado vital importancia la monitorización ambulatoria del paciente de alto riesgo, por lo que se han desarrollado tecnologías que faciliten esta labor como la creación de electrodos cada vez más cómodos y que originan menos artefactos, chips capaces de registrar la actividad eléctrica cardíaca y métodos de transmisión de esta información. Por último, se revisan los principales marcadores séricos de isquemia y el descubrimiento de nuevos, como el microARN-19a, obtenidos por PCR que son más sensibles y específicos que los anteriores

Estimation of Surrogate Respiration and Detection of Sleep Apnea Events from Dynamic Data Mining of Multiple Cardiorespiratory Sensors

This research investigates an approach to derive respiration waveform from heart sound signals, and compare the waveform signal obtained thus with those obtained from alternative methods for deriving respiration waveforms from measured ECG signals. The investigations indicate that HSR can lead to a cost effective alternative to the use of respiratory vests to analyze cardiorespiratory dynamics for clinical diagnostics and wellness assessments. The derived respiratory rate was further used to classify Type III sleep apnea periods using recurrence analysis. Detection of patterns causing sleep apnea could open up opportunities to researchers to better understand and predict symptoms leading to disorders linked with sleep apnea like hypertension, sudden infant death syndrome, high blood pressure and a risk of heart attack. Surrogate respiratory signals derived from heart sounds (HSR) are found to have 32% and 36% correlation with the actual respiratory signals recorded at upright and supine positions, respectively, as compared to EMD derived respiration signals (EDR) that have (18% and 26%) correlation with the respiration waveforms measured in upright and supine positions, respectively. Wavelet-derived respiration (WDR) signals show a higher wave-to-wave correlation (55% and 55%) than HSR and EDR waveforms, but the respiratory sinus arrhythmia (RSA), zero crossing intervals, and respiratory rates of the HSR correlate better with the measured values, compared with those from EDR and WDR signals. Three models were implemented using recurrence analysis to classify sleep apnea events and were compared with a vectorized time series derived model. Advanced predictive modeling tools like decision trees, neural networks and regression models were used to classify sleep apnea events form non-apneic events. Model comparison within preliminary analysis model consisting of nasal respiration as well as its time lagged components and heart rate when compared with recurrence models shows that the preliminary analysis model(vectorized time series) has a lower misclassification rate (10%) than the recurrence models( Model 1: 20% Model 2: 14%, Model 3: 12%).Industrial Engineering & Managemen

Diagnóstico temprano de la isquemia cardiaca

1. RESUMEN La cardiopatía isquémica causa más muertes, discapacidad y tiene un costo monetario mayor que cualquier otra enfermedad en los países desarrollados, siendo el infarto agudo de miocardio una de las entidades diagnosticadas con mayor frecuencia, por lo que su diagnóstico precoz ha sido un objetivo ampliamente estudiado durante años. En este trabajo se revisan y comparan los principales avances al respecto, repasando las técnicas y parámetros empleados clásicamente en el diagnóstico de la isquemia cardiaca. El electrocardiograma de 12 derivaciones es un elemento de importancia decisiva en el diagnóstico y clasificación del riesgo de los pacientes con sospecha de síndrome coronario agudo, pero tiene una serie de limitaciones importantes, tanto de sensibilidad (inicialmente del 28-65%), como de especificidad, por lo que han surgido nuevos parámetros electrocardiográficos, como el nuevo biomarcador eléctrico cardiaco (CEB), que con una sensibilidad del 85´3-94´4% ha demostrado tener una exactitud diagnóstica mayor que el ECG clásico, o el HFQRS y sus parámetros derivados, que también superan al ECG convencional con una sensibilidad del 75% ± 6% y una especificidad del 80% ± 6%. Se han estudiado nuevas localizaciones para los electrodos detectándose en 6 de ellas el mayor cambio del segmento ST registrado, de las que 5 no estaban en las posiciones estándar de las derivaciones precordiales. Se han desarrollado tecnologías que facilitan la monitorización ambulatoria del paciente de alto riesgo, como varios modelos de electrodos de cómoda colocación y biochips capaces de registrar la actividad eléctrica cardíaca, que han demostrado tener alta sensibilidad y exactitud diagnóstica mejorando además la capacidad de reducción de los artefactos registrados. Los biomarcadores sanguíneos no sólo complementan la evaluación clínica y electrocardiográfica, sino que juegan un papel diagnóstico esencial en el paciente con sospecha de síndrome coronario agudo. En esta revisión se recuerda la importancia de los biomarcadores empleados clásicamente en la detección de la isquemia cardiaca, siendo la troponina cardiaca I (cTnI) el “gold standard” en el diagnóstico del síndrome coronario agudo con una sensibilidad inicial del 20-50%. Asímismo, se estudian los avances biotecnológicos que han mejorado la capacidad de detección y cuantificación de daño miocárdico, como las troponinas de alta sensibilidad (hs-cTn), en cuyas mediciones seriadas se basan los nuevos algoritmos diagnósticos recomendados por la Sociedad Europea de Cardiología, y las nuevas líneas de investigación basadas en la vía genética de la patogénesis del infarto agudo de miocardio, donde el microRNA-19a, que ha demostrado ser más preciso para la presencia de infarto agudo de miocardio que los biomarcadores empleados clásicamente, y la proteína S100A4, con una sensibilidad del 76,3% y una especificidad del 87,5%, se postulan como nuevos biomarcadores fiables para la detección temprana de infarto agudo de miocardio

Mesure du rythme respiratoire sans contact

Une séance de téléréadaptation consiste à mettre en interaction une personne sous traitement dans son lieu de résidence avec un professionnel dans une clinique, via Internet, dans le but de mener à distance une consultation ou un traitement. Un exemple typique de séances de téléréadaptation implique la tenue d'exercices sur vélo stationnaire, accompagnés entre autres de la mesure de signes vitaux. Actuellement, le système utilisé par l'équipe de recherche en téléréadaptation de l'Université de Sherbrooke mesure seulement le rythme cardiaque ainsi que la saturation d'oxygène du patient. Pour que les cliniciens puissent avoir plus d'informations sur l'activité de la personne et donc pouvoir mieux adapter leurs consignes lors des séances, il faut que des informations supplémentaires soient ajoutées, telles que le rythme respiratoire, l'échelle de Borg, la vitesse et le niveau de résistance sélectionné lors de séances sur un vélo stationnaire. Ce projet de maîtrise porte sur une de ces mesures, soit celle du rythme respiratoire sans contact. La détection sans contact est moins gênante pour le patient, non biaisée et ne demande pas le port d'un capteur supplémentaire. L'objectif principal du projet est de développer une technique de mesure du rythme respiratoire sans contact utilisant une caméra thermique pan-tilt montée sur un trépied et placée devant le vélo stationnaire. Le système doit être capable de suivre la région bouche-nez d'une personne en temps réel lorsqu'elle est en mouvement grâce à un algorithme de suivi traitant une séquence d'images. La première étape a consisté à passer en revue les techniques envisageables pour mesurer le rythme respiratoire. Ensuite, il a été nécessaire de sélectionner une technique de mesure sans contact, de l'implémenter, de la rendre robuste aux mouvements, et de la tester en conditions réelles. Enfin, les performances du système développé ont été évaluées en comparant ce dernier avec une mesure provenant d'une ceinture respiratoire. Les résultats démontrent que le système fonctionne en temps réel lorsque le patient déplace ou effectue des rotations de sa tête sur le vélo stationnaire. Des recommandations sont faites pour minimiser les limitations du système, par exemple en cas de présence de personnes dans l'arrière-plan ou lorsque le patient parle. Le système réalisé est maintenant prêt pour être déployé lors de sessions de téléréadaptation à domicile afin de vérifier l'acceptabilité et la facilité d'utilisation du système