Polyvinylidene fluoride film based nasal sensor to monitor human respiration pattern: an initial clinical study

Wearable Sensors for Respiration Monitoring: A Review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

Wearable Multi-Functional Sensing Technology for Healthcare Smart Detection

In recent years, considerable research efforts have been devoted to the development of wearable multi-functional sensing technology to fulfill the requirements of healthcare smart detection, and much progress has been achieved. Due to the appealing characteristics of flexibility, stretchability and long-term stability, the sensors have been used in a wide range of applications, such as respiration monitoring, pulse wave detection, gait pattern analysis, etc. Wearable sensors based on single mechanisms are usually capable of sensing only one physiological or motion signal. In order to measure, record and analyze comprehensive physical conditions, it is indispensable to explore the wearable sensors based on hybrid mechanisms and realize the integration of multiple smart functions. Herein, we have summarized various working mechanisms (resistive, capacitive, triboelectric, piezoelectric, thermo-electric, pyroelectric) and hybrid mechanisms that are incorporated into wearable sensors. More importantly, to make wearable sensors work persistently, it is meaningful to combine flexible power units and wearable sensors and form a self-powered system. This article also emphasizes the utility of self-powered wearable sensors from the perspective of mechanisms, and gives applications. Furthermore, we discuss the emerging materials and structures that are applied to achieve high sensitivity. In the end, we present perspectives on the outlooks of wearable multi-functional sensing technology.

Detection of Low Cardiac Index using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively.

Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index

Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method.

Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor

Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monitoring. Further, because human experts manually classify sleep stages, it is time-consuming and exhibits inter-rater variability. Therefore, this article proposes a long short-term memory (LSTM) model for automatic sleep stage scoring using a polyvinylidene fluoride (PVDF) film sensor that can provide unconstrained long-term physiological monitoring. Signals were recorded using a PVDF sensor during PSG. From 60 recordings, 30 were used for training, 10 for validation, and 20 for testing. Sixteen parameters, including movement, respiration-related, and heart rate variability, were extracted from the recorded signals and then normalized. From the selected LSTM architecture, four sleep stage classification performances were evaluated for a test dataset and the results were compared with those of conventional machine learning methods. According to epoch-by-epoch (30 s) analysis, the classification performance for the four sleep stages had an average accuracy of 73.9% and a Cohen's kappa coefficient of 0.55. When compared with other machine learning methods, the proposed method achieved the highest classification performance. The use of LSTM networks with the PVDF film sensor has potential for facilitating automatic sleep scoring, and it can be applied for long-term sleep monitoring at home.

Recent Developments in Printing Flexible and Wearable Sensing Electronics for Healthcare Applications

Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers' health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics.

The Reliability of Polyvinylidene Fluoride Sensor for Intra- and Intersession Measurements

A new nasal sensor has been designed using Polyvinylidene fluoride (PVDF) film using its piezoelectric property to measure nasal patency. The aim of this study is to determine the intra- and intersession reliability of the new PVDF nasal sensor measurement of unilateral and combined nasal parameters in a group of healthy subjects. Two identical nasal sensors: for right nostril (RN) and left nostril (LN) were designed using piezoelectric natured PVDF films. Twenty subjects were studied. To evaluate the repeatability, total three sets of PVDF sensor measurements were recorded, two sets were taken 5 min apart during same session without repositioning the PVDF nasal sensors and two more sets were taken during 1 h apart successively, by repositioning the PVDF nasal sensor. Intraclass correlation coefficients (ICC) of PVDF sensor measurements for intra- and intersession showed a high and greater repeatability over time for all the combined (mean) and unilateral (RN and LN) values. In both healthy and patients, ICC values for both intra- and intersession measurements were ≥ 0.80 confirming strong reliability and also almost all of the coefficients of variation for the same parameters were low (below 10%). PVDF sensor measurements showed good intra- and intersession repeatability and can be recommended for the objective monitoring of nasal patency during diagnosis and follow-up of conditions.

Polyvinylidene fluoride sensor-based method for unconstrained snoring detection

We established and tested a snoring detection method using a polyvinylidene fluoride (PVDF) sensor for accurate, fast, and motion-artifact-robust monitoring of snoring events during sleep. Twenty patients with obstructive sleep apnea participated in this study. The PVDF sensor was located between a mattress cover and mattress, and the patients' snoring signals were unconstrainedly measured with the sensor during polysomnography. The power ratio and peak frequency from the short-time Fourier transform were used to extract spectral features from the PVDF data. A support vector machine was applied to the spectral features to classify the data into either the snore or non-snore class. The performance of the method was assessed using manual labelling by three human observers as a reference. For event-by-event snoring detection, PVDF data that contained 'snoring' (SN), 'snoring with movement' (SM), and 'normal breathing' epochs were selected for each subject. As a result, the overall sensitivity and the positive predictive values were 94.6% and 97.5%, respectively, and there was no significant difference between the SN and SM results. The proposed method can be applied in both residential and ambulatory snoring monitoring systems.

Validation of polyvinylidene fluoride nasal sensor to assess nasal obstruction in comparison with subjective technique

OBJECTIVE

The aim of this study is to validate the applicability of the PolyVinyliDene Fluoride (PVDF) nasal sensor to assess the nasal airflow, in healthy subjects and patients with nasal obstruction and to correlate the results with the score of Visual Analogue Scale (VAS).

METHODS

PVDF nasal sensor and VAS measurements were carried out in 50 subjects (25-healthy subjects and 25 patients). The VAS score of nasal obstruction and peak-to-peak amplitude (Vp-p) of nasal cycle measured by PVDF nasal sensors were analyzed for right nostril (RN) and left nostril (LN) in both the groups. Spearman's rho correlation was calculated. The relationship between PVDF nasal sensor measurements and severity of nasal obstruction (VAS score) were assessed by ANOVA.

RESULTS

In healthy group, the measurement of nasal airflow by PVDF nasal sensor for RN and LN were found to be 51.14±5.87% and 48.85±5.87%, respectively. In patient group, PVDF nasal sensor indicated lesser nasal airflow in the blocked nostrils (RN: 23.33±10.54% and LN: 32.24±11.54%). Moderate correlation was observed in healthy group (r=-0.710, p<0.001 for RN and r=-0.651, p<0.001 for LN), and moderate to strong correlation in patient group (r=-0.751, p<0.01 for RN and r=-0.885, p<0.0001 for LN).

CONCLUSION

PVDF nasal sensor method is a newly developed technique for measuring the nasal airflow. Moderate to strong correlation was observed between PVDF nasal sensor data and VAS scores for nasal obstruction. In our present study, PVDF nasal sensor technique successfully differentiated between healthy subjects and patients with nasal obstruction. Additionally, it can also assess severity of nasal obstruction in comparison with VAS. Thus, we propose that the PVDF nasal sensor technique could be used as a new diagnostic method to evaluate nasal obstruction in routine clinical practice.

Evaluation of polyvinylidene fluoride nasal sensor to assess deviated nasal septum in comparision with peak nasal inspiratory flow measurements

BACKGROUND

Deviated nasal septum (DNS) is one of the major causes of nasal obstruction. Polyvinylidene fluoride (PVDF) nasal sensor is the new technique developed to assess the nasal obstruction caused by DNS. This study evaluates the PVDF nasal sensor measurements in comparison with PEAK nasal inspiratory flow (PNIF) measurements and visual analog scale (VAS) of nasal obstruction.

METHODS

Because of piezoelectric property, two PVDF nasal sensors provide output voltage signals corresponding to the right and left nostril when they are subjected to nasal airflow. The peak-to-peak amplitude of the voltage signal corresponding to nasal airflow was analyzed to assess the nasal obstruction. PVDF nasal sensor and PNIF were performed on 30 healthy subjects and 30 DNS patients. Receiver operating characteristic was used to analyze the DNS of these two methods.

RESULTS

Measurements of PVDF nasal sensor strongly correlated with findings of PNIF (r = 0.67; p < 0.01) in DNS patients. A significant difference (p < 0.001) was observed between PVDF nasal sensor measurements and PNIF measurements of the DNS and the control group. A cutoff between normal and pathological of 0.51 Vp-p for PVDF nasal sensor and 120 L/min for PNIF was calculated. No significant difference in terms of sensitivity of PVDF nasal sensor and PNIF (89.7% versus 82.6%) and specificity (80.5% versus 78.8%) was calculated.

CONCLUSION

The result shows that PVDF measurements closely agree with PNIF findings. Developed PVDF nasal sensor is an objective method that is simple, inexpensive, fast, and portable for determining DNS in clinical practice.

Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor

We established and tested an unconstrained sleep apnea monitoring method using a polyvinylidene (PVDF) film-based sensor for continuous and accurate monitoring of apneic events occurred during sleep. Twenty-six sleep apnea patients and six normal subjects participated in this study. Subjects' respiratory signals were measured using the PVDF-based sensor during polysomnography. The PVDF sensor comprised a 4 × 1 array, and a thin silicon pad was placed over the sensor to prevent damage. Total thickness of the merged system was approximately 1.1 mm which was thin enough to prevent the subject from being consciously aware of its presence. It was designed to be placed under subjects' backs and installed between a bed cover and mattress. The proposed method was based on the standard deviation of the PVDF signals, and it was applied to a test set for detecting apneic events. The method's performance was assessed by comparing the results with a sleep physician's manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). The areas under the receiver operating curves at three AHI threshold levels (>5, >15, and >20) for sleep apnea diagnosis were 0.98, 0.99, and 0.98, respectively. For min-by-min apnea detection, the method classified sleep apnea with an average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. The developed system and method can be applied to sleep apnea detection in home or ambulatory monitoring.

Piezoelectric sensing: Evaluation for clinical investigation of deviated nasal septum

Noninvasive objective evaluation of nasal airflow is one of the important clinical aspects. The developed polyvinylidene fluoride (PVDF) sensor enables measurement of airflow through each side of the nose using its piezoelectric property. This study was designed to evaluate the diagnostic capability of the PVDF sensor in assessing the deviated nasal septum (DNS). PVDF nasal sensor uses its piezoelectric property to measure the peak-to-peak amplitude (V_p-p) of nasal airflow in both of the nostrils: right nostril (RN) and left nostril (LN), separately and simultaneously. We have compared the results of PVDF nasal sensor, visual analog scale (VAS), and clinician scale for 34 DNS patients and 28 healthy controls. Additionally, the results were further analyzed by receiver operating characteristic curve and correlation between PVDF nasal sensor and VAS in detecting DNS. We found a significant difference in the peak-to-peak amplitude values of the test group and the control group. The correlation between the PVDF nasal sensor measurements and VAS (RN and LN combined) for test group was statistically significant (−0.807; p < 0.001). Sensitivity and specificity of the PVDF nasal sensor measurements in the detection of DNS (RN and LN combined) was 85.3 and 74.4%, respectively, with optimum cutoff value ≤0.34 V_p-p. The developed PVDF nasal sensor is noninvasive and requires less patient efforts. The sensitivity and specificity of the PVDF nasal sensor are reliable. According to our findings, we propose that the said PVDF nasal sensor can be used as a new diagnostic tool to evaluate the DNS in routine clinical practice.