Breathing signal combining for respiration rate estimation in smart beds

Azimi, Hilda; Gilakjani, S. Soleimani; Bouchard, M.; Bennett, Stephanie L.; Goubran, Rafik; Knoefel, Frank

doi:10.1109/memea.2017.7985893

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…On the other hand, Figure 10 shows that placing the sensors as nodes without precise control of the distance between the sensor and the subject was adopted by 6% of the studies. Meanwhile, 29% of the studies integrated the sensors into mats or pillows [ 9 , 19 , 164 , 165 , 166 , 169 , 170 , 173 , 179 , 182 , 183 , 186 , 194 , 201 , 202 , 203 , 210 , 211 , 212 , 217 , 218 , 220 , 227 , 230 , 231 , 236 ] to measure breathing parameters during rest activities mainly. The rest of the environmental locations shown in Table 2 were only used in isolated cases.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 35 shows that “absolute error”, “relative/percentage error”, “Bland-Altman plot”, and “correlation coefficient” were the preferred formal metrics for wearable and environmental systems. The use of “root mean square error” [ 48 , 52 , 95 , 107 , 115 , 117 , 147 , 164 , 170 , 171 , 187 , 198 ], “linear regression” [ 68 , 161 , 167 , 183 , 209 ], and “accuracy” [ 7 , 76 , 133 , 161 , 179 , 190 , 193 , 207 , 216 ] was limited. Furthermore, the percentage of studies that provided an “informal” figure of merit was much higher for the wearable category (45%) than for the environmental group (17%).…”

Section: Resultsmentioning

confidence: 99%

“… 1 Note: The analysis for studies published before 2018 [ 5 , 6 , 7 , 9 , 10 , 19 , 48 , 50 , 51 , 52 , 53 , 54 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , …”

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Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Figureunclassified

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Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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“…For every 30 s signal segment with 50% overlap, all 72 signals of the PSM are weighted by a SNR-maximizing sensor signal combination method, with unequal weights based on the quality of their information [13]. Therefore, sensors with stronger signals (and better SNR) contribute more heavily than others.…”

Section: ) Signal Combination and Concatenationmentioning

confidence: 99%

“…These devices are unobtrusive and suitable for monitoring patients longitudinally without intervention [10]. Examples of these alternative sensors include digital video cameras for measuring the volume of air circulating into the lungs [11], non-contact radio-frequency sensors for measuring the biomotion caused by body movement and breathing [12], and pressure-sensitive mats (PSM) for measuring respiratory movements [13]. Among these sensors, the PSM sensing has advantages since it is capable of capturing body movements and breathing signals regardless of body position [14], and it does not compromise the privacy of patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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