Jakub Drzazga

Respiration monitoring systems play an important role in healthcare and fitness. For this purpose, resistive stretch sensors are frequently used, which are cheap and simple in operation. However, they are not free from drawbacks. Varying offset due to patient movement, low signal amplitude, as well as susceptibility to interference, can all pose serious challenges. In this paper, a novel signal conditioning circuit for a resistive respiration sensor is proposed that alleviates some of the above problems. Namely, the proposed low-frequency feedback topology improves the dynamic range by offset compensation, sustaining a high signal amplification. Further advantages of the new configuration are the phase shift of 0.5 degrees in the band of interest and higher gain for the respiration signal than for the offset. The topology was proved to correctly represent signal amplitude changes, as well as to be able to sample human respiration in the home environment. However, the circuit shows some nonlinear behavior around resistance discontinuity points–settling time after body position change of the patient, which can be as long as 40 s. The circuit was tested both in bench tests and in the prototype of a respiratory polygraphy device during actual sleep apnea examinations. The results indicate that resistive stretch sensors, along with low-frequency feedback topology, are a promising development path for future respiration monitoring devices.

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

This paper presents the components of a sleep apnea analysis device that have been evaluated for diagnosis suitability and expected difficulties. The medical background and statistics of sleep apnea are presented. Work related to the topic is then discussed. A description of the system components follows: ECG, respiratory effort, nasal flow and pulse oximetry. Problems expected with all of these measurements are then outlined both from the methodological and electronic perspective. The main difficulties identified are the necessity to maintain the exact location of the sensors on the patient’s body and vulnerability of the sensors to variations of the environmental conditions. Challenges on the electronic aspect are then discussed, which are mainly caused by the need of measuring small signals (noise, interference) and compensation of the sensors’ nonlinearity. Power supply design in the context of the measurement accuracy is also considered followed by an evaluation of the impact of telemedical function presence. The paper ends with a summary and conclusions.