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 · Aug 2015
Development of sleep apnea syndrome screening algorithm by using heart rate variability analysis and support vector machine.
Although sleep apnea syndrome (SAS) is a common sleep disorder, most patients with sleep apnea are undiagnosed and untreated because it is difficult for patients themselves to notice SAS in daily living. Polysomnography (PSG) is a gold standard test for sleep disorder diagnosis, however PSG cannot be performed in many hospitals. This fact motivates us to develop an SAS screening system that can be used easily at home. ⋯ In the proposed algorithm, various HRV features are derived from RRI data in both apnea and normal respiration periods of patients and healthy people, and an apnea/normal respiration (A/N) discriminant model is built from the derived HRV features by SVM. The result of applying the proposed SAS screening algorithm to clinical data demonstrates that it can discriminate patients with sleep apnea and healthy people appropriately. The sensitivity and the specificity of the proposed algorithm were 100% and 86%, respectively.
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Conf Proc IEEE Eng Med Biol Soc · Aug 2015
Evaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor.
Heart rate variability (HRV) provides significant information about the health status of an individual. Optical heart rate monitoring is a comfortable alternative to ECG based heart rate monitoring. However, most available optical heart rate monitoring devices do not supply beat-to-beat detection accuracy required by proper HRV analysis. ⋯ As compared to BG2, PO detected on average 99.57% of the heartbeats (0.43% of beats missed) and had 0.72% extra beat detection rate, with 5.94 ms mean absolute error (MAE) in beat-to-beat intervals (RRI) as compared to the ECG based RRI BG2. Mean RMSSD difference between PO and BG2 derived HRV was 3.1 ms. Therefore, PO provides an accurate method for long term HRV monitoring during sleep.
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Conf Proc IEEE Eng Med Biol Soc · Aug 2015
Modeling the impact of spinal cord stimulation paddle lead position on impedance, stimulation threshold, and activation region.
The effectiveness of spinal cord stimulation (SCS) for chronic pain treatment depends on selection of appropriate stimulation settings, which can be especially challenging following posture change or SCS lead migration. The objective of this work was to investigate the feasibility of using SCS lead impedance for determining the location of a SCS lead and for detecting lead migration, as well as the impact of axial movement and rotation of the St. Jude Medical PENTA™ paddle in the dorsal-ventral or medial-lateral directions on dorsal column (DC) stimulation thresholds and neural activation regions. ⋯ We found that SCS lead impedance was highly sensitive to the distance between the lead and cerebrospinal fluid (CSF) layer. In addition, among all the lead positions studied, medial-lateral movement resulted in the most substantial changes to SC activation regions. These results suggest that impedance can be used for detecting paddle position and lead migration, and therefore for guiding SCS programming.
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Conf Proc IEEE Eng Med Biol Soc · Aug 2015
Spatio-spectral characterization of local field potentials in the subthalamic nucleus via multitrack microelectrode recordings.
Deep brain stimulation of the subthalamic nucleus (STN) is a highly effective treatment for motor symptoms of Parkinson's disease. However, precise intraoperative localization of STN remains a procedural challenge. In the present study, local field potentials (LFPs) were recorded from three tracks during microelectrode recording-based (MER) targeting of STN, in five patients. ⋯ It's noted that the optimal track selection is not consistent with the track having highest beta band oscillations in two out of five subjects. In conclusion, microelectrode-derived LFP recordings may provide an alternative approach to single unit activity (SUA)-based MER, for localizing the target STN borders during DBS surgery. Despite the small number of subjects, the present study adds to existing knowledge about LFP-based pathophysiology of PD and its target-based spectral activities.
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This paper presents a novel patient-specific algorithm for prediction 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 a few electrodes. Each fragmented data clip is ten minutes in duration. ⋯ The algorithm is tested using intra-cranial EEG (iEEG) from the American Epilepsy Society Seizure Prediction Challenge database. The baseline experiment using a large number of features and RBF-SVM achieves a 100% sensitivity and an average AUC of 0.9985, while the proposed algorithm using only a small number of features and polynomial SVM with degree of 2 can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9795. For both experiments, only 10% of the available training data are used for training.