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
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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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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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.
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Conf Proc IEEE Eng Med Biol Soc · Aug 2015
Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography.
Sleep apnea syndrome (SAS) is regarded as one of the most common sleep-related breathing disorders, which can severely affect sleep quality. Since SAS is usually accompanied with the cyclical heart rate variation (HRV), many studies have been conducted on heart rate (HR) to identify it at an earlier stage. While most related work mainly based on clinical devices or signals (e.g., polysomnography (PSG), electrocardiography (ECG)), in this paper we focus on the ballistocardiographic (BCG) signal which is obtained in a non-invasive way. ⋯ The basic HRV features depict the ANS modulations on HR and Sample Entropy and Detrended Fluctuation Analysis are applied for the evaluations. All the extracted features along with personal factors are fed into the knowledge-based support vector machine (KSVM) classification model, and the prior knowledge is based on dataset distribution and domain knowledge. Experimental results on 42 subjects in 3 nights validate the effectiveness of the methods and features in identifying SAS (90.46% precision rate and 88.89% recall rate).
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Smart photoplethysmographic sensor for pulse wave registration at different vascular depths.
The aim of this paper is to propose a smart optical sensor for cardiovascular activity monitoring at different tissue layers. Photoplethysmography (PPG) is a noninvasive optical technique for monitoring mainly blood volume changes in the examined tissue. However, different important physiological parameters, such as oxygen saturation, heart and breathing rate, dynamics of skin micro-circulation, vasomotion activity etc., can be extracted from the registered PPG signal. ⋯ Compared to the existing sensors, the system enables to select the optimal LED (light emitting diode) and photo detector couple in order to obtain the pulse wave signal from the interested blood vessels with the highest possible signal to noise ratio. In this study, the designed PPG sensor was tested for the pulse wave registration from radial artery. The highest efficiency and signal to noise ratio was achieved using infrared LED (940 nm) and photo-diode pair.