Noninvasive monitoring of respiratory mechanics during sleep

ArticleinEuropean Respiratory Journal 24(6):1052-60 · January 2005with1 Reads
DOI: 10.1183/09031936.04.00072304 · Source: PubMed
Abstract
The sleep apnoea-hypopnoea syndrome is characterised by recurrent obstructions of the upper airway, resulting in sleep disruption and arterial oxygen desaturations. Noninvasive assessment of respiratory mechanics during sleep is helpful in facilitating the diagnosis and treatment of patients with sleep apnoea-hypopnoea syndrome. This series summarises the different tools that are currently available to noninvasively assess respiratory mechanics during sleep breathing disturbances. These techniques are classified according to the main variable monitored: ventilation, breathing effort or airway obstruction. Changes in patient ventilation are assessed by recording flow or volume signals by means of pneumotachographs, thermistors or thermocouples, nasal prongs or thoraco-abdominal bands. Common tools to noninvasively assess breathing efforts are the thoraco-abdominal bands and the pulse transit time technique. Upper airway obstruction is noninvasively characterised by its upstream resistance and its critical pressure or by means of the forced oscillation technique. Given the technical and practical limitations of each technique, combining different tools improves the reliability and robustness of patient assessment during sleep.
    • "Additionally, there are no surface loading effects that might reduce the accuracy of the measurement. The RR is defined as the number of breaths per minute and the typical RR at resting is 12 of a healthy person with a frequency of 0.2 Hz [6]. It has been noticed very often that there is a sudden degradation in RR leading to bradypnea (RR<12) while recovering from surgical anesthesia due to μ-opioid agonists used for pain control. "
    [Show abstract] [Hide abstract] ABSTRACT: Monitoring respiration rate in everyday life enables an early detection of the diseases and disorders that can suddenly appear as a life threatening episode. Respiratory Rate (RR) is defined as the number of breaths per minute and is a very important physiological parameter to be monitored in people both in healthy and critical condition, as it gives meaningful information regarding their respiratory system performance as well as condition. A typical RR for adult human being at rest is 12–20 and its corresponding frequency is 0.2 Hz approximately. During recovery from surgical anesthesia, a μ-opioid agonists used for pain control can slow down RR leading to bradypnea (RR < 12) or even apnea (cessation of respiration for an indeterminate period), while airway obstructions like asthma, emphysema and COPD. In all these cases long term monitoring can extend the capabilities of healthcare providers but only constraint lies with the performance reliability along with the economic barrier. In this chapter, a MEMS based capacitive nasal sensor system for measuring Respiration Rate (RR) of human being is developed. In order to develop such system, two identical arrays of diaphragms based MEMS capacitive nasal sensors are designed and virtually fabricated. A proposed schematic of the system consists of signal conditioning circuitry alongwith the sensors, is described here. In this proposed scheme, the two identical sensor arrays are mounted below Right Nostril (RN) and Left Nostril (LN), in such a way that the nasal airflow during inspiration and expiration impinge on the sensor diaphragms. Due to nasal airflow, the designed square diaphragm of the sensor is being deflected and thus induces a corresponding change in the original capacitance value. This change in capacitance value is be detected by a CMOS based clocked capacitance-to-voltage converter. The capacitive type MEMS sensors often suffer from stray and standing capacitive effect, in order to nullify this precision interface with MEMS capacitive pressure sensor, followed by an amplifier and a differential cyclic ADC is implemented to digitize the pressure information. The designed MEMS based capacitive nasal sensors is capable of identifying normal RR (18.5 ± 1.5 bpm) of human being. The design of sensors and its characteristics analysis are performed on a FEA/BEA based virtual simulation platform.
    Full-text · Chapter · Jul 2015 · Sensors
    • "PSG monitors body functions such as electroencephalography (EEG) patterns, electrooculography (EOG), major and minor muscle activity, heart rhythm and breathing activity. However, the measurement of these different parameters requires the use of multiple sensors that are worn or attached to suitable locations on the body8910111213. This necessitates some level of subject discomfort and may interfere with the primary behaviors of the subject, resulting in disturbance of the sleep monitoring. "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, an algorithm to extract respiration signals using a flexible projected capacitive sensing mattress (FPCSM) designed for personal health assessment is proposed. Unlike the interfaces of conventional measurement systems for poly-somnography (PSG) and other alternative contemporary systems, the proposed FPCSM uses projected capacitive sensing capability that is not worn or attached to the body. The FPCSM is composed of a multi-electrode sensor array that can not only observe gestures and motion behaviors, but also enables the FPCSM to function as a respiration monitor during sleep using the proposed approach. To improve long-term monitoring when body movement is possible, the FPCSM enables the selection of data from the sensing array, and the FPCSM methodology selects the electrodes with the optimal signals after the application of a channel reduction algorithm that counts the reversals in the capacitive sensing signals as a quality indicator. The simple algorithm is implemented in the time domain. The FPCSM system is used in experimental tests and is simultaneously compared with a commercial PSG system for verification. Multiple synchronous measurements are performed from different locations of body contact, and parallel data sets are collected. The experimental comparison yields a correlation coefficient of 0.88 between FPCSM and PSG, demonstrating the feasibility of the system design.
    Full-text · Article · Nov 2014
    • "The methods commonly used for measuring RR are visual observation, impedance pneumography, acoustic sensing, fiber optic sensing, Respiratory Inductance Plethysmograph (RIP) and nasal prongs (NP) [5]. However, due to very sensitive patients' movements and high cost, these methods find limited use in the clinical settings [6]. Earlier, Siivola [8], Choi and Jiang[8,9] used Poly Vinyl Di Flouride (PVDF) to record respiration and cardiac action in human beings. "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, a MEMS based capacitive nasal sensor system for measuring Respiration Rate (RR) of human being is developed. At first two identical diaphragm based MEMS capacitive nasal sensors are designed and virtually fabricated. A proposed schematic of the system consists of signal conditioning circuitry alongwith the sensors is described here. In order to measure the respiration rate the sensors are mounted below Right Nostril (RN) and Left Nostril (LN), in such a way that the nasal airflow during inspiration and expiration impinge on the sensor diaphragms. Due to nasal airflow, the designed square diaphragm of the sensor is being deflected and thus induces a corresponding change in the original capacitance value. This change in capacitance value is to be detected by a correlated-double-sampling (CDS) capacitance-to-voltage converter is designed for a precision interface with a MEMS capacitive pressure sensor, followed by an amplifier and a differential cyclic ADC to digitize the pressure information. The designed MEMS based capacitive nasal sensors is capable of identifying normal RR (18.5±1.5 bpm) of human being. The design of sensors and its characteristics analysis are performed in a FEA/BEA based virtual simulation platform.
    Full-text · Conference Paper · Sep 2014 · Sensors
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