A New Scheme for Determination of Respiration Rate in Human Being Using MEMS Based Capacitive Pressure Sensor

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.