Abstract:Abstract-Continuous monitoring of health state is important in elder and chronically ill (but otherwise not critical) patients. Ideally, the patient should not be aware of being continuously monitored. This study describes an automatic and non-conscious heart rate monitoring system based on the ballistocardiogram (BCG). The bi-axial BCG can be recorded by two piezoelectric force sensors and high-gain amplifiers. Digital signal preprocessing has been implemented to increase the SNR and an algorithm to automatic… Show more
“…During sleep, the pressure signal generated by chest and abdominal vibrations of the human body includes respiratory, BCG, and noise components. The frequency of the respiratory signal is about 0.3 and 2 Hz, and the highest frequency component of the BCG vital sign in the 1–20 Hz frequency range [ 21 ]. The noise components include human myoelectric noise, 50 Hz/60 Hz power-line interference, and body motion noise.…”
A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.
“…During sleep, the pressure signal generated by chest and abdominal vibrations of the human body includes respiratory, BCG, and noise components. The frequency of the respiratory signal is about 0.3 and 2 Hz, and the highest frequency component of the BCG vital sign in the 1–20 Hz frequency range [ 21 ]. The noise components include human myoelectric noise, 50 Hz/60 Hz power-line interference, and body motion noise.…”
A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.
“…We compared frequency variation related to heart and lung activity detected by our system with the other representing cardiopulmonary state physiology signal BCG, which also reflects the respiratory behavior variation [17]. We obtained the BCG data and frequency variation due to electromagnetic induction by our designed system when the target object is at normal breath state and holding breath state.…”
Section: Cardiopulmonary Signal Estimationmentioning
Cardiopulmonary signal contains vital and rich physiological information, which is very useful for clinical diagnosis and home healthcare. Convectional monitoring methods still need uncomfortable sensors or are expensive for nonprofessional common consumers. In this paper, we proposed a cardiopulmonary signal detection method based on electromagnetic induction. Based on thoracic volume variation affecting the biological impedance, which can be detected by magnetic induction, our idea is to develop a not complicated and practical measuring platform and exploit the intrinsic properties of cardiopulmonary signals with respiratory and heart rate. We have some theoretical analysis about the relationship of thoracic volume with biological impedance, sensor-head parameters, and the optimal measuring position. Then a whole measuring system has been designed and evaluated. The respiratory and heart rate obtained from the proposed method are not significantly different from the reference method (noncontact BCG).
“…Se colocaron sendos sensores piezoeléctricos en la cara posterior del respaldo y en la cara inferior del asiento de una silla y se utilizó un circuito electrónico basado en un amplificador de carga [8]. La Fig.…”
371Resumen-El balistocardiograma (BCG) es el registro de la fuerza de reacción del cuerpo ante la acción mecánica del corazón y del flujo de sangre de las principales arterias. Comúnmente el BCG es medido en dirección longitudinal y ha sido propuesto como una herramienta complementaria para estimar los cambios hemodinámicos de manera independiente o simultáneamente usando como referencia una señal cardiaca adicional. Sin embargo, las relaciones entre BCG en distintas direcciones no ha recibido mucha atención. En este trabajo se exploran las relaciones entre el BCG longitudinal y dorsovertral en personas. Se realizan dos análisis: solamente entre señales de BCG y entre ambas señales de BCG en conjunto con una señal de ECG. Los resultados indican que existe un alto índice de correlación entre las ondas que componen al BCG dorsoventral en relación al BCG longitudinal. Finalmente, ello parece establecer que la nomenclatura del BCG dorsoventral no sigue las mismas características del BCG longitudinal aunque esto amerita un análisis más profundo de las señales de estudio.Palabras clave-Análisis de señales cardiovasculares, Balistocardiograma, Procesamiento de señales.
I. INTRODUCCIÓNLa electrocardiografía es, quizá, la técnica más ampliamente estudiada para el estudio del funcionamiento cardiaco. La información aportada por el electrocardiograma (ECG) permite realizar diagnósticos sobre el estado de salud del corazón, sin embargo, sólo se obtiene la información de naturaleza eléctrica del mismo, un estudio más completo debería incluir información de otra naturaleza, como de origen mecánico.El balistocardiograma (BCG) es el registro de los movimientos del cuerpo debidos al latido cardiaco y al flujo de sangre por las principales arterias. Su principio de acción cumple con la tercera de Ley de Newton: a cada fuerza de acción, corresponde una fuerza de la misma magnitud en sentido contrario. El BCG fue descubierto a principios del siglo XX y se propuso como una herramienta para el estudio no invasivo de la actividad cardiovascular [1] pero fue abandonado la década de 1960 debido a que el registro es muy susceptible a artefactos de movimiento, frente a técnicas invasivas como la cateterización, y no invasivas, como la ecocardiografía.La tecnología actual y el desarrollo de la misma permiten reconsiderar el uso de la balistocardiografia como una herramienta para el estudio no invasivo de parámetros cardiovasculares.Las ondas presentes en el BCG son nombradas con letras consecutivas desde la H hasta la N [2], siendo la onda H, la primera deflexión positiva después del complejo QRS del ECG. La Fig. 1 muestra una forma de onda típica del BCG medido en el asiento de una silla. La flecha vertical indica la ubicación temporal de la onda R del ECG medido simultáneamente.Las fuerzas de reacción del cuerpo ocasiona que éste se mueva en tres direcciones: longitudinal o vertical, transversal o dorsoventral y lateral.
UnidadesArbritarias. Fig. 1. Forma del BCG longitudinal medido en posición sentada. La flecha de dirección indica la p...
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