Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks

Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this paper, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state of the art. Furthermore, when these predictions are mapped to the apnea-hypopnea index, a considerable improvement in per-patient scoring is achieved over conventional methods. This paper presents a powerful aid for trained staff to quickly diagnose sleep apnea

Detection of respiratory phases to estimate breathing pattern parameters using wearable bioimpendace

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMany studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance— This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters.Peer ReviewedPostprint (author's final draft

Analysis of time delay between bioimpedance and respiratory volume signals under inspiratory loaded breathing

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksBioimpedance is known for its linear relation with volume during normal breathing. For that reason, bioimpedance can be used as a noninvasive and comfortable technique for measuring respiration. The goal of this study is to analyze the temporal behavior of bioimpedance measured in four different electrode configurations during inspiratory loaded breathing. We measured four bioimpedance channels and airflow simultaneously in 10 healthy subjects while incremental inspiratory loads were imposed. Inspiratory loading threshold protocols are associated with breathing pattern changes and were used in respiratory mechanics studies. Consequently, this respiratory protocol allowed us to induce breathing pattern changes and evaluate the temporal relationship of bioimpedance with volume. We estimated the temporal delay between bioimpedance and volume respiratory cycles to evaluate the differences in their temporal behavior. The delays were computed as the lag which maximize the cross-correlation of the signals cycle by cycle. Six of the ten subjects showed delays in at least two different inspiratory loads. The delays were dependent on electrode configuration, hence the appearance of the delays between bioimpedance and volume were conditioned to the location and geometry of the electrode configuration. In conclusion, the delays between these signals could provide information about breathing pattern when breathing conditions change.Peer ReviewedPostprint (author's final draft

Chest movement and respiratory volume both contribute to thoracic bioimpedance during loaded breathing

Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.Peer ReviewedPostprint (published version

Wearable bioimpedance measurement for respiratory monitoring during inspiratory loading

Bioimpedance is an unobtrusive noninvasive technique to measure respiration and has a linear relation with volume during normal breathing. The objective of this paper was to assess this linear relation during inspiratory loading protocol and determine the best electrode configuration for bioimpedance measurement. The inspiratory load is a way to estimate inspiratory muscle function and has been widely used in studies of respiratory mechanics. Therefore, this protocol permitted us to evaluate bioimpedance performance under breathing pattern changes. We measured four electrode configurations of bioimpedance and airflow simultaneously in ten healthy subjects using a wearable device and a standard wired laboratory acquisition system, respectively. The subjects were asked to perform an incremental inspiratory threshold loading protocol during the measurements. The load values were selected to increase progressively until the 60% of the subject's maximal inspiratory pressure. The linear relation of the signals was assessed by Pearson correlation (r) and the waveform agreement by the mean absolute percentage error (MAPE), both computed cycle by cycle. The results showed a median greater than 0.965 in r coefficients and lower than 11 % in the MAPE values for the entire population in all loads and configurations. Thus, a strong linear relation was found during all loaded breathing and configurations. However, one out of the four electrode configurations showed robust results in terms of agreement with volume during the highest load. In conclusion, bioimpedance measurement using a wearable device is a noninvasive and a comfortable alternative to classical methods for monitoring respiratory diseases in normal and restrictive breathing.Postprint (published version

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective: Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods: Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax ) after the walking, and the HR decay 3 min after (HRR3 ). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient’s 6MWT outcomes. Results: Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax ) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3 ), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions: We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

Breathing pattern estimation using wearable bioimpedance for assessing COPD severity

Breathing pattern has been shown to be different in chronic obstructive pulmonary disease (COPD) patients compared to healthy controls during rest and walking. In this study we evaluated respiratory parameters and the breathing variability of COPD patients as a function of their severity. Thoracic bioimpedance was acquired on 66 COPD patients during the performance of the six-minute walk test (6MWT), as well as 5 minutes before and after the test while the patients were seated, i.e. resting and recovery phases. The patients were classified by their level of airflow limitation into moderate and severe groups. We characterized the breathing patterns by evaluating common respiratory parameters using only wearable bioimpedance. Specifically, we computed the median and the coefficient of variation of the parameters during the three phases of the protocol, and evaluated the statistical differences between the two COPD severity groups. We observed significant differences between the COPD severity groups only during the sitting phases, whereas the behavior during the 6MWT was similar. Particularly, we observed an inverse relationship between breathing pattern variability and COPD severity, which may indicate that the most severely diseased patients had a more restricted breathing compared to the moderate patients.This work was supported in part by the Universities and Research Secretariat from the Ministry of Business and Knowledge/Generalitat de Catalunya under the Grant GRC 2017 SGR 01770, in part by the Agencia Estatal de Investigacion from the Spanish Ministry of Science ánd Innovation and the European Regional Development Fund, under the Grants RTI2018 098472-B-I00 and PID2021-126455OB-I00, and in part by the CERCA Programme/Generalitat de Catalunya.Peer ReviewedPostprint (author's final draft