Physiological measurement
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Physiological measurement · Dec 2016
Comparative StudyComputerized wheeze detection in young infants: comparison of signals from tracheal and chest wall sensors.
Computerized wheeze detection is an established method for objective assessment of respiratory sounds. In infants, this method has been used to detect subclinical airway obstruction and to monitor treatment effects. The optimal location for the acoustic sensors, however, is unknown. ⋯ Comparison of wheeze rates measured over the trachea and the chest wall indicated strong correlation (r ⩾ 0.93, p < 0.001), with a bias of 1% or less and limits of agreement of within 3% for the inspiratory wheeze rate and within 6% for the expiratory wheeze rate. However, sounds from the chest wall were more often affected by disturbances than sounds from the trachea (23% versus 6%, p < 0.001). The study suggests that in young infants, a better quality of lung sound recordings can be obtained with the tracheal sensor.
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Physiological measurement · Nov 2016
A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.
This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. ⋯ When the signal-to-noise ratio is as low as -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.
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Physiological measurement · Nov 2016
Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA.
In this paper, classical time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmography signals (vPPG) were compared with heart rate variability (HRV) parameters extracted from ECG signals. The study focuses on the analysis of the changes observed during a rest-to-stand manoeuvre (a mild sympathetic stimulus) performed on 60 young, normal subjects (age: [Formula: see text] years). The objective is to evaluate if video-derived PRV indexes may replace HRV in the assessment of autonomic responses to external stimulation. ⋯ Finally, the power in the LF band (n.u.) was observed to increase significantly during standing by both HRV ([Formula: see text] versus [Formula: see text] (n.u.); rest versus standing) and PRV ([Formula: see text] versus [Formula: see text](n.u.); rest versus standing) analysis, but such an increase was lower in PRV parameters than that observed by HRV indexes. These results provide evidence that some differences exist between variability indexes extracted from HRV and video-derived PRV, mainly in the HF band during standing. However, despite these differences video-derived PRV indexes were able to evince the autonomic responses expected by the sympathetic stimulation induced by the rest-to-stand manoeuvre.
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Physiological measurement · Aug 2016
Suppression of false arrhythmia alarms in the ICU: a machine learning approach.
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. ⋯ For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.
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Physiological measurement · Aug 2016
Reduction of false arrhythmia alarms using signal selection and machine learning.
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. ⋯ The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.