Non-invasive continuous respiratory monitoring using temperature-based sensors.

Respiratory rate (RR) is a key vital sign that has been traditionally employed in the clinical assessment of patients and in the prevention of respiratory compromise. Despite its relevance, current practice for monitoring RR in non-intubated patients strongly relies on visual counting, which delivers an intermittent and error-prone assessment of the respiratory status. Here, we present a novel non-invasive respiratory monitor that continuously measures the RR in human subjects. The respiratory activity of the user is inferred by sensing the thermal transfer between the breathing airflow and a temperature sensor placed between the nose and the mouth. The performance of the respiratory monitor is assessed through respiratory experiments performed on healthy subjects. Under spontaneous breathing, the mean RR difference between our respiratory monitor and visual counting was 0.4 breaths per minute (BPM), with a 95% confidence interval equal to [- 0.5, 1.3] BPM. The robustness of the respiratory sensor to the position is assessed by studying the signal-to-noise ratio in different locations on the upper lip, displaying a markedly better performance than traditional thermal sensors used for respiratory airflow measurements.

Noninvasive ambulatory measurement system of cardiac activity.

This work implements a noninvasive system that measures the movements caused by cardiac activity. It uses unobtrusive Electro-Mechanical Films (EMFi) on the seat and on the backrest of a regular chair. The system detects ballistocardiogram (BCG) and respiration movements. Real data was obtained from 54 volunteers. 19 of them were measured in the laboratory and 35 in a hospital waiting room. Using a BIOPAC acquisition system, the ECG was measured simultaneously to the BCG for comparison. Wavelet Transform (WT) is a better option than Empirical Mode Decomposition (EMD) for signal extraction and produces higher effective measurement time. In the laboratory, the best results are obtained on the seat. The correlation index was 0.9800 and the Bland-Altman limits of agreement were 0.7136 ± 4.3673 [BPM]. In the hospital waiting room, the best results are also from the seat sensor. The correlation index was 0.9840, and the limits of agreement were 0.4386 ± 3.5884 [BPM]. The system is able to measure BCG in an unobtrusive way and determine the cardiac frequency with high precision. It is simple to use, which means the system can easily be used in non-standard settings: resting in a chair or couch, at the gym, schools or in a hospital waiting room, as shown.

Contact pressure monitoring device for sleep studies.

This project implements a non-invasive sleep monitoring system using a bed pressure sensor array. The system detects changes in the contact pressure between a subject and the bed and is able to automatically select the sensor with the best respiratory signal, determine the respiratory rate (RR), count number of sleep apneas and count body position changes through the night. The respiratory signal is validated with an airflow sensor using Pearson's correlation coefficient. To determine the performance of body position and apnea detection algorithms, the sensibility and positive predictivity is computed on preliminary data and known records from a Physionet database. Real data is obtained from 5 subjects totaling 39 hours measured at home during a full night sleep, in a non-invasive way. The data is used to calculate relevant parameters to estimate a sleep quality. Cumulative frequency of sleep interval duration is proposed as a novel metric for sleep assessment.