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A novel feature extraction algorithm for multichannel FPGA-based neural recording systems is presented in this paper. It contains the Dual Vertex Threshold (DVT) and the Minimum Delimitation (MD), which are used for spike detection and feature vector extraction respectively. By reducing the computational complexity of DVT and MD, the difficulty of this algorithm in application is greatly reduced....
We demonstrate a method for identifying and removing electroencephalograph (EEG) artifacts from mobile Brain Computer Interface (BCI) data with few channels. The main components of the method entail; one-time selection of visually inspected “good quality” EEG data from past experiments and outlier detection using statistical methods. The standardly used thresholding and filtering method was compared...
Multi-channel neural recording devices are widely used for in vivo neuroscience experiments. Incurred by high signal frequency and large channel numbers, the acquisition rate could be on the order of hundred MB/s, which requires compression before wireless transmission. In this paper, we adopt the Compressed Sensing framework with a simple on-chip implementation. To improve the performance while reducing...
The massive amount of data recorded by dense electrode arrays which are routinely connected to Nyquist-sampling signal conditioning blocks introduces new design challenges for implantable and wireless biological signal acquisition. Five different architectures of implantable multichannel neural recording systems are compared in terms of power and area constraints. Silicon results of a 16-channel spatial-domain...
Local Mean Decomposition (LMD) has long been proven as an effective method for the analysis of non-linear and non-stationary time series. In this work, an on-line version of LMD, called extended Sliding Local Mean Decomposition (eSLMD), is proposed. The property of eSLMD is examined through numerical simulations, and the performance is evaluated through the ECG noise removal with the test signal obtained...
In this paper we examine the relationship between Cerebral Blood Flow and image quality using Near-Infrared Spectroscopy (NIRS). We measured data of cerebral blood flow and applied it to image quality assessment. To prove that test subjects acknowledged the quality degradation of test images, we performed subjective assessment tests through questionnaires. Results showed that all subjects acknowledged...
Occlusion-effect and acoustic feedback are common complaints of the hearing aid user. The occlusion-effect is described as an annoying quality of the user's own voice that sounds hollow or boomy, while feedback instability results in an unpleasant loud continuous tone. Despite the availability of high performance feedback cancellers, severe to profound losses require large amplification and, as a...
This paper presents a novel method for ECG baseline drift removal while preserving the integrity of the ST segment. Baseline estimation is achieved by tracking 3 isoelectric points within the ECG waveform as fiducial markers used in an interpolation filter. These points are determined relative to the QRS complex, which is extracted using a known method (Pan-Tompkins algorithm). The proposed algorithm...
We have proposed a multi-stage motion-tolerant algorithm for reliable extraction of oxygen saturation. We introduce a novel cardiac gating algorithm to suppress effects of motion artifacts caused by venous blood movement on wearable oximetry sensors. This approach eliminates the motion artifact effectively and results in a much more accurate and reliable oxygen saturation readings compared to traditional...
Despite the emerging progress in many subfields of bionics, developing a working bionic voice prosthesis for voice-loss patients has remained an unresolved research question. A bionic voice prosthesis replaces the function of a missing larynx and generates natural voice for patients who retain a functional vocal tract but have lost their voice because of surgical removal of the larynx (laryngectomy)...
Stroke is the leading cause of death and disability worldwide. This requires significant resources in health-care costs. In addition to physical examination and brain imaging, medical staff need a more quantitative and continuous method to reveal and monitor the severity of patients. This paper proposed a novel stroke severity monitoring system based on a nonlinear method-quantitative modified multiscale...
The dynamic brain networks forming during wakefulness and anesthetic-induced unconsciousness are investigated using time-delayed correlation and graph theoretical measures. Electrical brain activity (EEG) from 10 patients under propofol anesthesia during routine surgery is characterized using the shortest path length, λ, and clustering, c, extracted from time-delayed correlation. An increase in λ...
The purpose of this study was the development and investigation of the automatic Premature Ventricular Contraction (PVC) detection and classification method using Photoplethysmographic (PPG) signals. The main issue of using PPG for arrhythmia detection are the artefacts which may be falsely detected as an arrhythmic pulses. The method is based on 6 PPG features, obtained in 12 s analysis frame. The...
This paper explores how noise can improve classification accuracy of motor imagery classification using an ensemble support vector machine (ESVM) classifier. We add white Gaussian noise to the EEG signals and use them with the original signal data set for the ESVM training process. The ESVM classifier uses coefficients of the discrete wavelet transform (DWT) and coefficients of the autoregressive...
Conditioning and processing of biological signals represent interesting challenges for wearable electronics in health applications. Information gathering from these signals requires complex hardware circuitry and dedicated computation resources. The design of innovative analog front-end integrated circuits, combined with efficient signal processing algorithms, allows the development of platforms for...
The automatic detection of sleep apnea episodes, without the need of polysomnography and outside a clinical facility, could help facilitate the diagnosis of this disorder. In this work, features to detect sleep apnea events were computed from respiration and electrocardiogram recordings acquired with a wearable smart-shirt. First, a classical scheme exploiting the amplitude decrease of the respiration...
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