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
A practical algorithm to reduce false critical ECG alarms using arterial blood pressure and/or photoplethysmogram waveforms.
There has been a high rate of false alarms for the critical electrocardiogram (ECG) arrhythmia events in intensive care units (ICUs), from which the 'crying-wolf' syndrome may be resulted and patient safety may be jeopardized. This article presents an algorithm to reduce false critical arrhythmia alarms using arterial blood pressure (ABP) and/or photoplethysmogram (PPG) waveform features. We established long duration reference alarm datasets which consist of 573 ICU waveform-alarm records (283 for development set and 290 for test set) with total length of 551 patent days. ⋯ At the time of a critical ECG alarm, the corresponding EFI values of those ABP/PPG pulses around the alarm time are checked for adjudicating (accept/reject) this alarm. The algorithm retains all (100%) the true alarms and significantly reduces the false alarms. Our results suggest that the algorithm is effective and practical on account of its real-time dynamic processing mechanism and computational efficiency.
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Physiological measurement · Aug 2016
Detection of false arrhythmia alarms with emphasis on ventricular tachycardia.
Our approach to detecting false arrhythmia alarms in the intensive care unit breaks down into several tasks. It involves beat detection on different signals: electrocardiogram, photoplethysmogram and arterial blood pressure. The quality of each channel has to be estimated in order to evaluate the reliability of obtained beat detections. ⋯ This feature was important in order to reduce misclassification of ventricular beats: there was an improvement in the ventricular tachycardia alarm true positive rate from 69% to 81%. However, the true negative rate was reduced from 95% to 69% and our global challenge score (real-time event) dropped from 79.02 to 74.28. Our challenge algorithm achieved the third best score in the 2015 PhysioNet/CinC challenge event 1 (real time).