Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram.
Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice. ⋯ Our proposed methodology resulted in a mean-absolute-error (MAE) of 1.98 breaths per minute (bpm), outperformed other fusing strategies (mean fusion: 2.95 bpm; median fusion: 2.33 bpm; ML: 2.30 bpm). It also outperformed the best single algorithm (2.39 bpm) and the benchmark algorithm proposed for use with Capnobase (2.22 bpm). We conclude that the proposed fusion methodology can be used to combine RR estimates from multiple sources derived from the PPG, to infer a reliable and robust estimation of the respiratory rate in an unsupervised manner.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Seizure detection using regression tree based feature selection and polynomial SVM classification.
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG signals from three or four electrodes. Each fragmented data clip is one second in duration. ⋯ The algorithm is tested using the intra-cranial EEG (iEEG) from the UPenn and Mayo Clinic's Seizure Detection Challenge database. It is shown that the proposed algorithm can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9818, a mean detection horizon of 5.8 seconds, and a specificity of 99.9% on using half of the training data for classification. The proposed approach also achieved a mean AUC of seizure detection and early seizure detection of 0.9136 on the testing data.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
An improved artifact removal algorithm for continuous cardiac output and blood pressure recordings.
Measurement artifacts are common in hemodynamic recordings such as cardiac output and blood pressure. Manual artifact removal is cumbersome for large datasets, and automatic processing using algorithms may reduce workload and provide more reproducible outcomes. This paper presents an artifact removal algorithm which is more aggressive compared to a previously described method. ⋯ Precision, recall and F-score was determined by agreement with manual inspection by an expert. Based on the total of all measurements from CO and MAP by LiDCO and CO and MAP by Nexfin, precision was 86%, 79%, 79% and 68% respectively (87%, 62%, 76% and 58% for the reference method), recall was 97%, 94%, 89% and 97% (31%, 6%, 28% and 6% for reference), F-score was 91%, 85%, 84% and 80% (46%, 10%, 41% and 10% for reference). The proposed algorithm offers an improved performance in removing true artifacts, in some cases a reduced ability to preserve true measurements, but an improved overall accuracy.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Estimation of physiological sub-millimeter displacement with CW Doppler radar.
Doppler radar physiological sensing has been studied for non-contact detection of vital signs including respiratory and heartbeat rates. This paper presents the first micrometer resolution Wi-Fi band Doppler radar for sub-millimeter physiological displacement measurement. A continuous-wave Doppler radar working at 2.4GHz is used for the measurement. ⋯ A mechanical mover was used as target, and programmed to conduct sinusoidal motions to simulate pulse motions. Measured displacements were compared with a reference system, which indicates a superior performance in accuracy for having absolute errors less than 10μm, and relative errors below 4%. It indicates the feasibility of highly accurate non-contact monitoring of physiological movements using Doppler radar.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Sleep apnea detection using time-delayed heart rate variability.
Sleep apnea is a sleep disorder distinguished by repetitive absence of breathing. Compared with the traditional expensive and cumbersome methods, sleep apnea diagnosis or screening with physiological information that can be easily acquired is needed. This paper describes algorithms using heart rate variability (HRV) to automatically detect sleep apneas as long as it can be easily acquired with unobtrusive sensors. ⋯ Experiments were conducted with a data set of 23 sleep apnea patients using support vector machine (SVM) classifiers and cross validations. Results show that using eleven HRV features with a time delay of 1.5 minutes rather than the features without time delay for SA detection, the overall accuracy increased from 74.9% to 76.2% and the Cohen's Kappa coefficient increased from 0.49 to 0.52. Further, an accuracy of 94.5% and a Kappa of 0.89 were achieved when applying subject-specific classifiers.