scholarly journals Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

10.2196/13737 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e13737 ◽  
Author(s):  
Joseph Prinable ◽  
Peter Jones ◽  
David Boland ◽  
Cindy Thamrin ◽  
Alistair McEwan
Keyword(s):  
Machine Learning ◽  
Respiratory Rate ◽  
Short Term Memory ◽  
Reference Signal ◽  
Left Index Finger ◽  
Expiration Time ◽  
Respiratory Conditions ◽  
Breathing Monitoring ◽  
Term Monitoring

Background There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

scholarly journals Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology (Preprint)

2019 ◽  
Author(s):  
Joseph Prinable ◽  
Peter Jones ◽  
David Boland ◽  
Cindy Thamrin ◽  
Alistair McEwan
Keyword(s):  
Machine Learning ◽  
Respiratory Rate ◽  
Short Term Memory ◽  
Reference Signal ◽  
Left Index Finger ◽  
Expiration Time ◽  
Respiratory Conditions ◽  
Breathing Monitoring ◽  
Term Monitoring

BACKGROUND There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. OBJECTIVE In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. METHODS A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. RESULTS Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). CONCLUSIONS A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

scholarly journals Derivation of Respiratory Metrics in Health and Asthma

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7134
Author(s):  
Joseph Prinable ◽  
Peter Jones ◽  
David Boland ◽  
Alistair McEwan ◽  
Cindy Thamrin
Keyword(s):  
Machine Learning ◽  
Respiratory Rate ◽  
Short Term Memory ◽  
Reference Signal ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Strongly Correlated ◽  
Monitoring Methods ◽  
Expiration Time ◽  
Wide Variability

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.

Derivation of Respiratory Metrics in Health and Asthma; Machine Learning Methodology (Preprint)

2020 ◽  
Author(s):  
Joseph Prinable ◽  
Peter Jones ◽  
David Boland ◽  
Alistair McEwan ◽  
Cindy Thamrin
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Reference Signal ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Local Health ◽  
Strongly Correlated ◽  
Human Research Ethics ◽  
Expiration Time ◽  
Wide Variability

BACKGROUND The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. OBJECTIVE Examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. METHODS Pulse oximetry data was collected from 11 healthy and 11 asthma subjects who breathed at a range of controlled respiratory rates. UNET and Long Short-Term memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. RESULTS The UNET vs LSTM model provided breathing metrics which were strongly correlated with those from the reference signal (all p<0.001, except for inspiratory:expiratory ratio). The following relative mean bias(95% confidence interval) were observed: inspiration time 1.89(-52.95, 56.74)% vs 1.30(-52.15, 54.74)%, expiration time -3.70(-55.21, 47.80)% vs -4.97(-56.84, 46.89)%, inspiratory:expiratory ratio -4.65(-87.18, 77.88)% vs -5.30(-87.07, 76.47)%, inter-breath intervals -2.39(-32.76, 27.97)% vs -3.16(-33.69, 27.36)%, and respiratory rate 2.99(-27.04 to 33.02)% vs 3.69(-27.17 to 34.56)%. CONCLUSIONS Both machine learning models show strongly correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g. by increasing the size of the training dataset at the lower breathing rates. CLINICALTRIAL Sydney Local Health District Human Research Ethics Committee (#LNR\16\HAWKE99 ethics approval).

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

A new method for long-term monitoring of the ballistocardiogram, heart rate, and respiration

1981 ◽  
Vol 240 (5) ◽  
pp. R384-R392 ◽  
Author(s):  
J. Alihanka ◽  
K. Vaahtoranta ◽  
I. Saarikivi
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Respiratory Movement ◽  
Body Movements ◽  
Ultralow Frequency ◽  
Wave Forms ◽  
The Subject ◽  
Long Term Monitoring ◽  
Term Monitoring

The principle of the static charge-sensitive-bed (SCSB) method is described. The method is simple and inexpensive. From the SCSB recording the ballistocardiogram (BCG) and respiratory movement can be simultaneously recorded by selective filtering of the original signal. The SCSB recording thus enables continuous long-term monitoring of the BCG, heart rate, respiratory rate, respiratory amplitude, and body movements. There are no electrodes or cables connected to the subject. The SCSB-BCG signal was studied by comparing the SCSB method with the conventional ultralow-frequency (ULF) acceleration BCG. The wave forms at rest and the amplitude responses to physical exercise were studied by both methods. Multiple recordings of the BCG, heart rate, respiratory movement, respiratory rate, and body movements after exercise and during sleep using the SCSB method are presented. The SCSB method opens new approaches to long-term studies of the regulation of myocardial performance, heart rate, and respiration. Applications of the SCSB method for clinical sleep studies, patient monitoring and cardiovascular screening examinations are discussed.

scholarly journals The Prediction of Oceanic Mesoscale Eddy Properties and Propagation Trajectories Based on Machine Learning

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2521
Author(s):  
Xin Wang ◽  
Huizan Wang ◽  
Donghan Liu ◽  
Wenke Wang
Keyword(s):  
Machine Learning ◽  
Ocean Circulation ◽  
Prediction Models ◽  
Short Term Memory ◽  
Mesoscale Eddy ◽  
Absolute Error ◽  
Trajectory Data ◽  
Short Term ◽  
Prediction Ability

Mesoscale eddies play an important role in ocean circulation, material energy exchange and variation of ocean environments. Machine learning methods can efficiently process massive amounts of data and automatically learn the implicit features, thus providing a new approach to eddy prediction research. Using the mesoscale eddy trajectory data derived from multimission satellite altimetry, we propose relevant machine learning models based on long short-term memory network (LSTM) and the extra trees (ET) algorithm for the prediction of eddy properties and propagation trajectories. Characteristic factors, including attribute features and past eddy displacements, were exploited to construct prediction models with high effectiveness and few predictors. To study their effects at different forecasting times, we separately trained the models by rebuilding the corresponding relationship between eddy samples and labels. In addition, the variation characteristics and the predictability of eddy properties and propagation trajectories were discussed from the prediction results. Cross-validation shows that at different prediction times, our method is superior to previous methods in terms of the mean absolute error (MAE) of eddy properties and the root mean square error (RMSE) of propagation. The stable variation in eddy properties makes the prediction more dependent on the historical time series than that of a propagation forecast. The short-term propagation prediction of eddies contained more noise than long-term predictions, and the long-term predictions revealed a more significant trend. Finally, we discuss the effect of eddy properties on the prediction ability of the eddy propagation trajectory.

scholarly journals Detection and Quantification of Resting Tremor in Parkinson’s Disease Using Long-Term Acceleration Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Han Yuan ◽  
Sen Liu ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Detection Model ◽  
Resting Tremor ◽  
Learning Classifiers ◽  
Acceleration Data ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Detection And Quantification

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

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