Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing). © 2013 IEEE

Effects of glaucoma and snoring on cerebral oxygenation in the visual cortex: a study using functional Near Infrared Spectroscopy (fNIRS)

Purpose: The purpose of this study was to investigate the effects of snoring and glaucoma on the visual Haemodynamic Response (HDR) using functional Near Infrared Spectroscopy (fNIRS). Methods: We recruited 8 glaucoma patients (aged 56-79), 6 habitual snorers (aged 26-61) and 10 healthy control participants (aged 21-78). Glaucoma patients were of varying subtypes and under care of ophthalmologists. Prior to testing visual acuity, blood pressure, heart rate and a medical history were taken. HDRs were recorded over the primary visual cortex (V1) using a reversing checkerboard paradigm. Results & Discussion: All participants showed the characteristic increase of Oxyhaemoglobin concentration ([HbO]) and decrease of Deoxyhaemoglobin concentration ([HbR]) during visual stimulation (p < 0.001, η2 = 0.78). Despite this, there were signifi cant group differences with a large effect size (η2 = 0.28). During visual stimulation normal participants had greater [HbO] compared to snorers and glaucoma patients (p < 0.01). Both glaucoma patients and snorers presented with comparable HDR for [HbO] and [HbR] in V1. Importantly, during visual stimulation, the increased [HbO] in glaucoma patients correlated well with their visual fi elds and self-reported activities of daily living (r = -0.98, r = -0.82, p < 0.05). Both glaucoma patients and snorers presented with an attenuated HDR in V1. Our results suggest a possible vascular link between these conditions

Non-contact Dual Pulse Doppler System Based Real-time Relative Demodulation and Respiratory & Heart Rates Estimations for Chronic Heart Failure Patients

Long-term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. Most of monitoring systems have shortcomings related to their functionality and/or patient comfortably. Non-contact monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for chronic heart failure (CHF) patients. This paper presents novel real-time demodulation technique and estimations algorithms for the non-contact physiological vital signs assessments for CHF patients based on a patented novel non-contact bio-motion sensor. A database consists of twenty CHF patients with New York Heart Association (NYHA) Heart Failure Classification Class II & III, whose underwent full Polysomnography (PSG) analysis for the diagnosis of sleep apnea, disordered sleep, or both, were selected for the study. The propose algorithms analyze the non-contact bio-motion signals and estimate the patient's respiratory and heart rates. The outputs of the algorithms are compared with gold-standard PSG recordings. Across all twenty CHF patients' recordings, the respiratory rate estimation median accuracy achieved 91.52% with median error of ±1.31 breaths per minute. The heart rate estimation median accuracy achieved 91.29% with median error of ±6.16 beats per minute. A potential application would be home continuous sleep and circadian rhythm monitoring

Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep

Peer ReviewedPostprint (author's final draft

Adversarial Unsupervised Representation Learning for Activity Time-Series

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new source, making it possible to track the user's lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activity2vec that learns and "summarizes" the discrete-valued activity time-series. It learns the representations with three components: (i) the co-occurrence and magnitude of the activity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with adversarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with arXiv:1712.0952

Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed

Assessment of respiratory flow cycle morphology in patients with chronic heart failure

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.Peer ReviewedPostprint (author's final draft