Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals

Respiratory Waveform Estimation From Multiple Accelerometers: An Optimal Sensor Number and Placement Analysis

Respiratory patterns are commonly measured to monitor and diagnose cardiovascular, metabolic, and sleep disorders. Electronic devices such as masks used to record respiratory waveforms usually require medical staff support and obstruct the patients' breathing, causing discomfort. New techniques are being investigated to overcome such limitations. An emerging approach involves accelerometers to estimate the respiratory waveform based on chest motion. However, most of the existing techniques employ a single accelerometer placed on an arbitrary thorax position. The present work investigates the use and optimal placement of multiple accelerometers located on the thorax and the abdomen. The study population is composed of 30 healthy volunteers in three different postures. By means of a custom-made microcontrolled system, data are acquired from an array of ten accelerometers located on predefined positions and a pneumotachograph used as reference. The best sensor locations are identified by optimal linear reconstruction of the reference waveform from the accelerometer data in the minimum mean square error sense. The analysis shows that right-hand side locations contribute more often to optimal respiratory waveform estimates, a sound finding given that the right lung has a larger volume than the left lung. In addition, we show that the respiratory waveform can be blindly extracted from the recorded accelerometer data by means of independent component analysis. In conclusion, linear processing of multiple accelerometers in optimal positions can successfully recover respiratory information in clinical settings, where the use of masks may be contraindicated.

Smart Vest for Respiratory Rate Monitoring of COPD Patients Based on Non-Contact Capacitive Sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were −0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal's amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects' RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm. Graphical abstract ᅟ.

Respiratory signal derived from the smartphone built-in accelerometer during a Respiratory Load Protocol

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

Estimation of bilateral asynchrony between diaphragm mechanomyographic signals in patients with chronic obstructive pulmonary disease

The aim of the present study was to measure bilateral asynchrony in patients suffering from Chronic Obstructive Pulmonary Disease (COPD) performing an incremental inspiratory load protocol. Bilateral asynchrony was estimated by the comparison of respiratory movements derived from diaphragm mechanomyographic (MMGdi) signals, acquired by means of capacitive accelerometers placed on left and right sides of the rib cage. Three methods were considered for asynchrony evaluation: Lissajous figure, Hilbert transform and Motto's algorithm. Bilateral asynchrony showed an increase at 20, 40 and 60% (values of normalized inspiratory pressure by their maximum value reached in the last inspiratory load) while the very severe group showed an increase at 20, 40, 80, and 100 % during the protocol. These increments in the phase's shift can be due to an increase of the inspiratory load along the protocol, and also as a consequence of distress and fatigue. In summary, this work evidenced the capability to estimate bilateral asynchrony in COPD patients. These preliminary results also showed that the use of capacitive accelerometers can be a suitable sensor for recording of respiratory movement and evaluation of asynchrony in COPD patients.