Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

Jarchi, Delaram; Rodgers, Sarah; Tarassenko, Lionel; Clifton, David A.

doi:10.1109/jsen.2018.2828599

Cited by 49 publications

(36 citation statements)

References 34 publications

(38 reference statements)

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“…The SSA algorithm has shown to be highly useful for denoising in various applications such as motion [ 32 ], heart rate [ 31 ], and respiration analysis [ 19 ]. The input to the SSA algorithm is the time-series and a value for the embedding dimension.…”

Section: Methodsmentioning

confidence: 99%

“…To estimate respiratory rate, the ECG, PPG, and acceleration signals have been used in various research studies [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] among many others. While various methods in the literature have used windowed segments of the PPG/ECG signals, very few methods have investigated the instantaneous respiratory rate (IRR) from ECG [ 20 , 21 ] or PPG [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Jarchi

Salvi

Tarassenko

et al. 2018

Sensors

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Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Jarchi

Salvi

Tarassenko

et al. 2018

Sensors

Self Cite

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“…Using wearable devices to monitor breathing is currently an active area of research that is focused mainly on sleep apnea [ 4 , 5 , 6 , 7 , 10 ] and respiratory rate detection [ 11 , 12 ]. Sleep apnea studies, however, typically only classify sensor signals as representing either apnea or non-apnea, without specifying where in time the events begin and end.…”

Section: Related Workmentioning

confidence: 99%

“…Most prior studies have been designed to classify breathing events as being either normal or abnormal using binary classification techniques. The typical approach is to use a sliding window of single or multiple lengths to detect events [ 4 , 5 , 6 , 7 ]. The drawback of this approach, however, is the computational complexity and complicated implementation that varies based on event length, event overlap, and event type.…”

Section: Introductionmentioning

confidence: 99%

“…The field of medicine probably most in need of automated methods of breathing pattern detection and classification is that concerned with sleep apnea [ 4 , 5 , 6 ]. The public health burden of sleep apnea is difficult to estimates accurately because many cases go undiagnosed, and it varies dramatically ion severity, but it is widely accepted that it affects millions of people in the US and is increasing because of the rise in the incidence of obesity [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

McClure

Erdreich

Bates

et al. 2020

Sensors

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Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.

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Ambulatory and Popular Sensor Measurements

2020

Body Sensor Networking, Design and Algorithms

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Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring

Cited by 49 publications

References 34 publications

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

Ambulatory and Popular Sensor Measurements

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