An algorithm for extracting the PPG Baseline Drift in real-time

Photoplethysmography is an optical technique for measuring the perfusion of blood in skin and tissue arterial vessels. Due to its simplicity, accessibility and abundance of information on an individual’s cardiovascular system, it has been a pervasive topic of research within recent years. With these benefits however there are many challenges concerning the processing and conditioning of the signal in order to allow information to be extracted. One such challenge is removing the baseline drift of the signal, which is caused by respiratory rate, muscle tremor and physiological changes within the body as a response to various stimuli. Over the years there have been many methods developed in order to condition the signal such as Wavelet Transform, Cubic Spline Interpolation, Morphological Operators and Fourier-Based filtering techniques. All have their own individual benefits and drawbacks. These drawbacks are that they are unsuitable for real-time usage due to the computation power needed, or have the trade-off of being real-time at the cost of deforming the signal which is unideal for accurate analysis. This thesis aims to explore these techniques in order to develop an algorithm that can be used to condition the signal against the baseline drift in real-time, while being able to achieve good computational efficiency and the preservation of the signal form

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

Real-time FPGA Implementation of a Digital Self-interference Canceller in an Inband Full-duplex Transceiver

Full-duplex is a communications engineering scheme that allows a single device to transmit and receive at the same time, using the same frequency for both tasks. Compared to traditionally used half-duplex, where the transmission and reception is divided temporally or spectrally, the spectral efficiency may theoretically be doubled in full-duplex operation. However, the technology suffers from a profound problem, namely the self-interference (SI) signal, which is the name given to the signal a node transmits and simultaneously also receives. Making the full-duplex technology feasible demands that the SI signal is mitigated with SI cancellers. Such cancellers reconstruct an estimate of the SI signal and subtract the estimate from the received signal, thus suppressing the SI. For the SI signal to be diminished as much as possible, canceller solutions should be deployed in both analog and digital domains. This thesis presents a digital real-time implementation of a novel nonlinear self-interference canceller, based on splines interpolation. This canceller utilizes a Hammerstein model to identify the SI signal, taking advantage of a FIR filter for the identification of the SI channel, and splines interpolation to model the nonlinear effects of the transceiver circuitry. The new canceller solution promises great reduction in computational complexity compared to traditional algorithms with little to no sacrifice in cancellation performance. The algorithm was implemented for a National Instruments USRP SDR device using LabVIEW Communications System Design Suite 2.0. The LabVIEW program provides the required connectivity to the USRP platform, as the SDR lacks a user interface. In addition, the functionality of the SDR is determined in LabVIEW, by creating code that is then run on the USRP, or more specifically, on the built-in FPGA of the device. The FPGA is where the SI canceller is executed, in order to ensure real-time operation. Even though the USRP device employs a high-end FPGA with plenty of resources, the canceller implementation needs to be simplified nonetheless, for example by approximating magnitudes of complex values and by decreasing the sample rate of the canceller. With the simplifications, the implementation utilizes only 34.9 % of available slices on the FPGA and only 34.6 % of the DSP units. Measurements with the canceller show that it is capable of SI cancellation of up to 48 dB, which is on par with state-of-the-art real-time SI cancellations in literature. Furthermore, it was demonstrated that the canceller is capable of bidirectional communication in various circumstances

Adaptive kernel canonical correlation analysis algorithms for nonparametric identification of Wiener and Hammerstein systems

This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA) emerges as the logical solution to this problem.We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications